A machine learning approach to estimating HI deficiency in galaxies | Paper Citation Record

Observation 29357981-83d7-45ab-8801-61d60cbba07e · outbound

This paper cites The Arecibo Legacy Fast ALFA Survey: The ALFALFA Extragalactic HI Source Catalog.

A machine learning approach to estimating HI deficiency in galaxies The Arecibo Legacy Fast ALFA Survey: The ALFALFA Extragalactic HI Source Catalog

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.024714Z

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Observation 6a1371fa-0058-46f4-bb64-90438d471df4 · outbound

This paper cites an unresolved cited work.

A machine learning approach to estimating HI deficiency in galaxies Unresolved cited work

Reference 2

Resolution: verified exact; doi, observed 2026-07-09T11:06:10.878527Z

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Observation 9c8c5352-e244-4863-a224-ab7ccce96c52 · outbound

This paper cites The Arecibo Galaxy Environment Survey (AGES) XI: the expanded Abell 1367 field. Data catalogue and HI census over the surveyed volume.

A machine learning approach to estimating HI deficiency in galaxies The Arecibo Galaxy Environment Survey (AGES) XI: the expanded Abell 1367 field. Data catalogue and HI census over the surveyed volume

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.909140Z

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Observation 9a12de2d-ec43-4a44-bf0a-d14caf906a36 · outbound

This paper cites , keywords =.

A machine learning approach to estimating HI deficiency in galaxies , keywords =

Reference 4

Resolution: verified exact; doi, observed 2026-07-09T11:06:10.998750Z

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Observation 2702d8cd-15a1-4234-99c0-a69af5eaf205 · outbound

This paper cites The Herschel Virgo Cluster Survey: II. Truncated dust disks in HI-deficient spirals.

A machine learning approach to estimating HI deficiency in galaxies The Herschel Virgo Cluster Survey: II. Truncated dust disks in HI-deficient spirals

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.939108Z

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Observation 25a5b680-f2cb-4b4f-a4bb-7a1fcd7b79b7 · outbound

This paper cites R., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15167.x , http://adsabs.harvard.edu/abs/2009MNRAS.398..607V 398, 607.

A machine learning approach to estimating HI deficiency in galaxies R., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15167.x , http://adsabs.harvard.edu/abs/2009MNRAS.398..607V 398, 607

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:10.929888Z

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Observation 74b719c7-20ae-4672-97ad-d37b4c388acb · outbound

This paper cites , keywords =.

A machine learning approach to estimating HI deficiency in galaxies , keywords =

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T11:06:11.210104Z

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Observation 3c82aee2-1115-4d8a-bcd2-521d9518e31a · outbound

This paper cites , keywords =.

A machine learning approach to estimating HI deficiency in galaxies , keywords =

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T11:06:11.218297Z

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Observation 1e235ca7-83cc-4230-a9cb-32650ae7c55c · outbound

This paper cites an unresolved cited work.

A machine learning approach to estimating HI deficiency in galaxies Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-07-09T11:06:11.214272Z

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Observation a8653c54-be73-4d97-b368-ab251cb1e202 · outbound

This paper cites Environmental Effects on Late-Type Galaxies in Nearby Clusters.

A machine learning approach to estimating HI deficiency in galaxies Environmental Effects on Late-Type Galaxies in Nearby Clusters

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.027146Z

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Observation 7db56eaf-8110-41b7-ba38-3a2e5344f1ab · outbound

This paper cites , keywords =.

A machine learning approach to estimating HI deficiency in galaxies , keywords =

Reference 11

Resolution: verified exact; doi, observed 2026-07-09T11:06:11.035413Z

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Observation 8a5249e6-d5d0-4d7d-b9cb-80e11812a701 · outbound

This paper cites Friends-of-friends galaxy group finder with membership refinement. Application to the local Universe.

A machine learning approach to estimating HI deficiency in galaxies Friends-of-friends galaxy group finder with membership refinement. Application to the local Universe

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.921834Z

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Observation 747c783f-bcd6-489b-b1bd-f4b86d7c3cff · outbound

This paper cites Bayesian group finder based on marked point processes. Method and feasibility study using the 2MRS data set.

A machine learning approach to estimating HI deficiency in galaxies Bayesian group finder based on marked point processes. Method and feasibility study using the 2MRS data set

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.019689Z

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Observation 68b9eba6-53a1-42ff-a172-07c584595d35 · outbound

This paper cites An Extended Halo-based Group/Cluster finder: application to the DESI legacy imaging surveys DR8.

A machine learning approach to estimating HI deficiency in galaxies An Extended Halo-based Group/Cluster finder: application to the DESI legacy imaging surveys DR8

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.918003Z

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Observation c03d09b9-93e1-4bf3-8eab-2322bff0e87b · outbound

This paper cites Merging groups and clusters of galaxies from the SDSS data. The catalogue of groups and potentially merging systems.

A machine learning approach to estimating HI deficiency in galaxies Merging groups and clusters of galaxies from the SDSS data. The catalogue of groups and potentially merging systems

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.851672Z

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Observation 20dd8c8a-bcbf-46c9-8ace-ee0e59124632 · outbound

This paper cites A catalog of 132,684 clusters of galaxies identified from Sloan Digital Sky Survey III.

A machine learning approach to estimating HI deficiency in galaxies A catalog of 132,684 clusters of galaxies identified from Sloan Digital Sky Survey III

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.900861Z

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Observation dea4a3d7-431b-4b74-82a9-b2880b4510a9 · outbound

This paper cites Groups and clusters of galaxies in the SDSS DR8. Value-added catalogues.

A machine learning approach to estimating HI deficiency in galaxies Groups and clusters of galaxies in the SDSS DR8. Value-added catalogues

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.993665Z

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Observation f014cac4-886c-4ded-b043-d23e6c9c0871 · outbound

This paper cites redMaPPer I: Algorithm and SDSS DR8 Catalog.

A machine learning approach to estimating HI deficiency in galaxies redMaPPer I: Algorithm and SDSS DR8 Catalog

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.016891Z

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Observation 2ff1ce67-6a28-4f2f-bd6b-b58dba49880c · outbound

This paper cites and Shibahashi , H.

A machine learning approach to estimating HI deficiency in galaxies and Shibahashi , H

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:10.977167Z

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Observation 43a50148-cb68-414a-bfe2-c18b84a53ae4 · outbound

This paper cites Color Separation of Galaxy Types in the Sloan Digital Sky Survey Imaging Data.

A machine learning approach to estimating HI deficiency in galaxies Color Separation of Galaxy Types in the Sloan Digital Sky Survey Imaging Data

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.009358Z

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Observation e07257d4-c2e8-4067-a3f2-7240a6b5a0c9 · outbound

This paper cites Statistical Properties of Bright Galaxies in the SDSS Photometric System.

A machine learning approach to estimating HI deficiency in galaxies Statistical Properties of Bright Galaxies in the SDSS Photometric System

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.951806Z

Source-reported events for the cited work

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Observation 4de79652-94cb-4b35-b517-03523cbcd6a5 · outbound

This paper cites T., Ogilvie G.

A machine learning approach to estimating HI deficiency in galaxies T., Ogilvie G

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:11.014466Z

Source-reported events for the cited work

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Observation 6cb6f3a5-9395-468c-9573-43798780831f · outbound

This paper cites Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey.

A machine learning approach to estimating HI deficiency in galaxies Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.959444Z

Source-reported events for the cited work

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Observation ef40540d-7dd3-4fee-92ca-b29d1c4e5c16 · outbound

This paper cites Improving galaxy morphologies for SDSS with Deep Learning.

A machine learning approach to estimating HI deficiency in galaxies Improving galaxy morphologies for SDSS with Deep Learning

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.004454Z

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Observation 4d439656-5005-4460-96eb-5b60251e09d4 · outbound

This paper cites A Catalog of Detailed Visual Morphological Classifications for 14034 Galaxies in the Sloan Digital Sky Survey.

A machine learning approach to estimating HI deficiency in galaxies A Catalog of Detailed Visual Morphological Classifications for 14034 Galaxies in the Sloan Digital Sky Survey

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.912821Z

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Observation ee6bc7c6-ad3c-4daa-ba30-4b8b54876fbf · outbound

This paper cites year = 1963, month = apr, volume =.

A machine learning approach to estimating HI deficiency in galaxies year = 1963, month = apr, volume =

Reference 26

Resolution: verified exact; doi, observed 2026-07-09T11:06:10.963235Z

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Observation 57e73e82-b9c0-4eee-91fc-a8c0fa736fe4 · outbound

This paper cites Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning.

A machine learning approach to estimating HI deficiency in galaxies Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.985469Z

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Observation 207e735e-8605-4403-bec7-a4261a4e36f0 · outbound

This paper cites Scikit-learn: Machine Learning in Python.

A machine learning approach to estimating HI deficiency in galaxies Scikit-learn: Machine Learning in Python

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.935262Z

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Observation 2170793d-f7f6-4e61-ad85-b0464fc21883 · outbound

This paper cites Machine Learning 45(1), 5–32 (Oct 2001).

A machine learning approach to estimating HI deficiency in galaxies Machine Learning 45(1), 5–32 (Oct 2001)

Reference 29

Resolution: verified exact; doi, observed 2026-07-09T11:06:11.042325Z

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Observation 660210a4-9190-4b31-aff6-37b50daadf00 · outbound

This paper cites Probabilistic Random Forest: A machine learning algorithm for noisy datasets.

A machine learning approach to estimating HI deficiency in galaxies Probabilistic Random Forest: A machine learning algorithm for noisy datasets

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.014516Z

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Observation 22ac9af4-a1b3-4a82-b70f-fedd94849f95 · outbound

This paper cites New Lessons from the HI Size-Mass Relation of Galaxies.

A machine learning approach to estimating HI deficiency in galaxies New Lessons from the HI Size-Mass Relation of Galaxies

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.018950Z

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Observation cccfe76d-c18b-41b7-a7c5-8e1f760b50ad · outbound

This paper cites Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe.

A machine learning approach to estimating HI deficiency in galaxies Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.902382Z

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Observation eb424be7-1cd2-4ba3-b8ad-431bee38fa07 · outbound

This paper cites Boletin de la Asociacion Argentina de Astronomia La Plata Argentina , year = 1963, month = feb, volume =.

A machine learning approach to estimating HI deficiency in galaxies Boletin de la Asociacion Argentina de Astronomia La Plata Argentina , year = 1963, month = feb, volume =

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T11:06:11.216453Z

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Observation 53041248-8228-4738-ab0b-68bba4626583 · outbound

This paper cites Annales d'Astrophysique , year = 1948, month = jan, volume =.

A machine learning approach to estimating HI deficiency in galaxies Annales d'Astrophysique , year = 1948, month = jan, volume =

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T11:06:11.216262Z

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Observation ba704656-b84e-44a9-bc10-0087254d364d · outbound

This paper cites R., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15167.x , http://adsabs.harvard.edu/abs/2009MNRAS.398..607V 398, 607.

A machine learning approach to estimating HI deficiency in galaxies R., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15167.x , http://adsabs.harvard.edu/abs/2009MNRAS.398..607V 398, 607

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:10.977678Z

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Observation 34457cb9-2897-493d-a4d1-9d2292815e65 · outbound

This paper cites xGASS: Total cold gas scaling relations and molecular-to-atomic gas ratios of galaxies in the local Universe.

A machine learning approach to estimating HI deficiency in galaxies xGASS: Total cold gas scaling relations and molecular-to-atomic gas ratios of galaxies in the local Universe

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.001432Z

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Observation eb328e11-6aca-4cba-bfe1-34cb573de928 · outbound

This paper cites The cold interstellar medium of galaxies in the Local Universe.

A machine learning approach to estimating HI deficiency in galaxies The cold interstellar medium of galaxies in the Local Universe

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.953334Z

Source-reported events for the cited work

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Observation c47572ef-83e0-40f0-97de-9c916fae34fd · outbound

This paper cites xCOLD GASS: the complete IRAM-30m legacy survey of molecular gas for galaxy evolution studies.

A machine learning approach to estimating HI deficiency in galaxies xCOLD GASS: the complete IRAM-30m legacy survey of molecular gas for galaxy evolution studies

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.011919Z

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Observation b9348267-5730-4859-b7f2-fc7c686e2958 · outbound

This paper cites The HI Content of Spirals. II. Gas Deficiency in Cluster Galaxies.

A machine learning approach to estimating HI deficiency in galaxies The HI Content of Spirals. II. Gas Deficiency in Cluster Galaxies

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.980095Z

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Observation 65b72033-9f90-4695-b138-546de15a4f2c · outbound

This paper cites HI-deficient galaxies in intermediate density environments.

A machine learning approach to estimating HI deficiency in galaxies HI-deficient galaxies in intermediate density environments

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.991485Z

Source-reported events for the cited work

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Observation 6f4652ea-66de-43ff-bc85-28e951ff701a · outbound

This paper cites HI Deficiencies and Asymmetries in HIPASS Galaxies.

A machine learning approach to estimating HI deficiency in galaxies HI Deficiencies and Asymmetries in HIPASS Galaxies

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.904928Z

Source-reported events for the cited work

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Observation c8b7b76d-cf42-4d01-a22b-cb718a185dde · outbound

This paper cites The GALEX Arecibo SDSS Survey. VIII. Final Data Release -- The Effect of Group Environment on the Gas Content of Massive Galaxies.

A machine learning approach to estimating HI deficiency in galaxies The GALEX Arecibo SDSS Survey. VIII. Final Data Release -- The Effect of Group Environment on the Gas Content of Massive Galaxies

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.974338Z

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Observation 11732c29-8da2-40bb-b090-206699efa044 · outbound

This paper cites Gas-regulation of galaxies: the evolution of the cosmic sSFR, the metallicity-mass-SFR relation and the stellar content of haloes.

A machine learning approach to estimating HI deficiency in galaxies Gas-regulation of galaxies: the evolution of the cosmic sSFR, the metallicity-mass-SFR relation and the stellar content of haloes

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.030337Z

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Observation 746d2aea-f7a6-41b2-881a-a8914fb7cba9 · outbound

This paper cites BUDHIES IV: Deep 21-cm neutral Hydrogen, optical and UV imaging data of Abell 963 and Abell 2192 at z $\simeq$ 0.2.

A machine learning approach to estimating HI deficiency in galaxies BUDHIES IV: Deep 21-cm neutral Hydrogen, optical and UV imaging data of Abell 963 and Abell 2192 at z $\simeq$ 0.2

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.945822Z

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Observation c4dee49f-e2b1-4e00-87d7-79c022a96290 · outbound

This paper cites Origin of the galaxy HI size-mass relation.

A machine learning approach to estimating HI deficiency in galaxies Origin of the galaxy HI size-mass relation

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.910178Z

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Observation 4646e7ae-3f27-4d05-865d-cb6d3802aebb · outbound

This paper cites A., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18706.x , http://adsabs.harvard.edu/abs/2011MNRAS.417.1621D 417.

A machine learning approach to estimating HI deficiency in galaxies A., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18706.x , http://adsabs.harvard.edu/abs/2011MNRAS.417.1621D 417

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:10.996388Z

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Observation 7ea67ca3-c266-42c7-8cba-3658f938304a · outbound

This paper cites T., Ogilvie G.

A machine learning approach to estimating HI deficiency in galaxies T., Ogilvie G

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:11.039034Z

Source-reported events for the cited work

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Observation f9034654-a454-4b4d-96b2-757e132df545 · outbound

This paper cites K-corrections and filter transformations in the ultraviolet, optical, and near infrared.

A machine learning approach to estimating HI deficiency in galaxies K-corrections and filter transformations in the ultraviolet, optical, and near infrared

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.898055Z

Source-reported events for the cited work

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Observation 328c98ef-a5f5-4d84-8b2c-64757af9baec · outbound

This paper cites an unresolved cited work.

A machine learning approach to estimating HI deficiency in galaxies Unresolved cited work

Reference 49

Resolution: verified exact; doi, observed 2026-07-09T11:06:10.940296Z

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Observation e47e513c-6178-454e-9c82-38d897157719 · outbound

This paper cites P., & Geller, M.

A machine learning approach to estimating HI deficiency in galaxies P., & Geller, M

Reference 50

Resolution: verified exact; doi, observed 2026-07-09T11:06:11.031215Z

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Observation 2cbafe31-e336-41bf-96ee-8a783367fd7d · outbound

This paper cites B., Einasto, J., & Shandarin, S.

A machine learning approach to estimating HI deficiency in galaxies B., Einasto, J., & Shandarin, S

Reference 51

Resolution: verified exact; doi, observed 2026-07-09T11:06:11.020914Z

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Observation d2c2efba-bbc9-4688-869a-583329c6be94 · outbound

This paper cites A., Phillips M.

A machine learning approach to estimating HI deficiency in galaxies A., Phillips M

Reference 52

Resolution: verified exact; doi, observed 2026-07-09T11:06:11.003690Z

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Observation 6d8bca34-66b7-4968-99f0-17d64cb50056 · outbound

This paper cites Mignone and Jonathan C.

A machine learning approach to estimating HI deficiency in galaxies Mignone and Jonathan C

Reference 53

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Observation 80de06cb-6244-4f28-b762-4fcb9c32be1b · outbound

This paper cites The AMIGA sample of isolated galaxies XIII. The HI content of an almost "nurture free" sample.

A machine learning approach to estimating HI deficiency in galaxies The AMIGA sample of isolated galaxies XIII. The HI content of an almost "nurture free" sample

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.001956Z

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Observation 44758711-925b-47aa-80c4-1b30cc49b728 · outbound

This paper cites The Effect of Ram-Pressure Stripping on Dwarf Galaxies.

A machine learning approach to estimating HI deficiency in galaxies The Effect of Ram-Pressure Stripping on Dwarf Galaxies

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.989117Z

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Observation 13e4bdcd-d2e1-4875-92f7-34a704136f67 · outbound

This paper cites Uncovering Additional Clues to Galaxy Evolution. II. The Environmental Impact of the Virgo Cluster on the Evolution of Dwarf Irregular Galaxies.

A machine learning approach to estimating HI deficiency in galaxies Uncovering Additional Clues to Galaxy Evolution. II. The Environmental Impact of the Virgo Cluster on the Evolution of Dwarf Irregular Galaxies

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.996940Z

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Observation 458a904a-639e-4327-868f-51f05deda8e7 · outbound

This paper cites On the Assembly of Dwarf Galaxies in Clusters and their Efficient Formation of Globular Clusters.

A machine learning approach to estimating HI deficiency in galaxies On the Assembly of Dwarf Galaxies in Clusters and their Efficient Formation of Globular Clusters

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.979793Z

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Observation 6a962afc-1f7a-43d3-b9fa-79f9cbf1bfa4 · outbound

This paper cites Mass and Environment as Drivers of Galaxy Evolution II: The quenching of satellite galaxies as the origin of environmental effects.

A machine learning approach to estimating HI deficiency in galaxies Mass and Environment as Drivers of Galaxy Evolution II: The quenching of satellite galaxies as the origin of environmental effects

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.890457Z

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Observation b228322e-530d-4f00-92a4-fb44a26a80c0 · outbound

This paper cites Virgo Filaments I: Processing of gas in cosmological filaments around the Virgo cluster.

A machine learning approach to estimating HI deficiency in galaxies Virgo Filaments I: Processing of gas in cosmological filaments around the Virgo cluster

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.033909Z

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Observation e9069f28-ef5e-4781-a4c5-7ffcddf3c21b · outbound

This paper cites and Shibahashi , H.

A machine learning approach to estimating HI deficiency in galaxies and Shibahashi , H

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:11.022225Z

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Observation 374b5aa1-c782-4967-92f4-f4dbbc3e24b9 · outbound

This paper cites The Effect of Filaments and Tendrils on the HI Content of Galaxies.

A machine learning approach to estimating HI deficiency in galaxies The Effect of Filaments and Tendrils on the HI Content of Galaxies

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.903542Z

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Observation 4339b8a2-ab15-41c5-ab2d-7af49fd96025 · outbound

This paper cites The Arecibo Legacy Fast ALFA Survey: I. Science Goals, Survey Design and Strategy.

A machine learning approach to estimating HI deficiency in galaxies The Arecibo Legacy Fast ALFA Survey: I. Science Goals, Survey Design and Strategy

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.927987Z

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Observation cec3f94b-ca4a-4aaf-a02e-f5e471b735ae · outbound

This paper cites The Sloan Digital Sky Survey: Technical Summary.

A machine learning approach to estimating HI deficiency in galaxies The Sloan Digital Sky Survey: Technical Summary

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.040198Z

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Observation 7284f839-2fb7-4af1-97fa-c27a3f252563 · outbound

This paper cites , keywords =.

A machine learning approach to estimating HI deficiency in galaxies , keywords =

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-07-09T11:06:11.033386Z

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Observation ff9f629b-0456-439b-b23a-3ed02a71712c · outbound

This paper cites The Luminosity Function of Galaxies in SDSS Commissioning Data.

A machine learning approach to estimating HI deficiency in galaxies The Luminosity Function of Galaxies in SDSS Commissioning Data

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.025593Z

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Observation 517d95f4-5d30-417e-97e6-5ba5b873b3ff · outbound

This paper cites The Arecibo Legacy Fast ALFA Survey: The alpha.40 HI Source Catalog, its Characteristics and their Impact on the Derivation of the HI Mass Function.

A machine learning approach to estimating HI deficiency in galaxies The Arecibo Legacy Fast ALFA Survey: The alpha.40 HI Source Catalog, its Characteristics and their Impact on the Derivation of the HI Mass Function

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.011749Z

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Observation 309af351-85ce-4452-800b-a2d9fadfce17 · outbound

This paper cites On the completeness and reliability of visual source extraction : an examination of eight thousand data cubes by eye.

A machine learning approach to estimating HI deficiency in galaxies On the completeness and reliability of visual source extraction : an examination of eight thousand data cubes by eye

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.880464Z

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Observation 35c9b3ae-8c35-4c2a-ab77-30e6b0ef024d · outbound

This paper cites Catalog of Nearby Isolated Galaxies in the Volume z<0.01.

A machine learning approach to estimating HI deficiency in galaxies Catalog of Nearby Isolated Galaxies in the Volume z<0.01

Reference 69

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T11:06:11.027893Z

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Observation f6e24fd7-5acc-4372-aac7-2c86c9d73323 · outbound

This paper cites A., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18706.x , http://adsabs.harvard.edu/abs/2011MNRAS.417.1621D 417.

A machine learning approach to estimating HI deficiency in galaxies A., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18706.x , http://adsabs.harvard.edu/abs/2011MNRAS.417.1621D 417

Reference 70

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:10.930585Z

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Observation 29c9471d-7b2a-4680-b90f-01baed3004b4 · outbound

This paper cites and Shibahashi , H.

A machine learning approach to estimating HI deficiency in galaxies and Shibahashi , H

Reference 71

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:10.933244Z

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Observation b56fbb16-b565-46b5-ae3c-2cba4571c9ad · outbound

This paper cites The HI Content of Spirals. I. Field-Galaxy HI Mass Functions and HI Mass-Optical Size Regression.

A machine learning approach to estimating HI deficiency in galaxies The HI Content of Spirals. I. Field-Galaxy HI Mass Functions and HI Mass-Optical Size Regression

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.006940Z

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Observation 464f3a4c-45ce-4eef-9a7d-b3e91b99c2b3 · outbound

This paper cites New HI scaling relations to probe the HI content of galaxies via global HI-deficiency maps.

A machine learning approach to estimating HI deficiency in galaxies New HI scaling relations to probe the HI content of galaxies via global HI-deficiency maps

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.986780Z

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Observation cf943b9d-0371-4319-8d42-c81c60e8c4a2 · outbound

This paper cites HI Content and Optical Properties of Field Galaxies from the ALFALFA Survey. II. Multivariate Analysis of a Galaxy Sample in Low Density Environments.

A machine learning approach to estimating HI deficiency in galaxies HI Content and Optical Properties of Field Galaxies from the ALFALFA Survey. II. Multivariate Analysis of a Galaxy Sample in Low Density Environments

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.009173Z

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Observation 44a16e46-2768-43a8-82cb-95512847fe86 · outbound

This paper cites DSPS: Differentiable Stellar Population Synthesis.

A machine learning approach to estimating HI deficiency in galaxies DSPS: Differentiable Stellar Population Synthesis

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.037918Z

Source-reported events for the cited work

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Observation 7df6153c-28f9-47ce-97ef-d14f5fff2add · outbound

This paper cites The effects of the cluster environment on the galaxy mass-size relation in MACSJ J1206.2-0847.

A machine learning approach to estimating HI deficiency in galaxies The effects of the cluster environment on the galaxy mass-size relation in MACSJ J1206.2-0847

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.990972Z

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Observation c9271fcc-c593-478d-abeb-95d9cfcbdfe3 · outbound

This paper cites and Shibahashi , H.

A machine learning approach to estimating HI deficiency in galaxies and Shibahashi , H

Reference 77

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T11:06:10.982463Z

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Observation f8d9de0d-0009-4839-a4f0-7c196649a112 · outbound

This paper cites The Size Evolution of Star-forming and Quenched Galaxies in the IllustrisTNG simulation.

A machine learning approach to estimating HI deficiency in galaxies The Size Evolution of Star-forming and Quenched Galaxies in the IllustrisTNG simulation

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.036207Z

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Observation c764316d-c2ae-4628-850d-86d097802bfa · outbound

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A machine learning approach to estimating HI deficiency in galaxies archivePrefix = "arXiv", eprint =

Reference 79

Resolution: verified exact; doi, observed 2026-07-09T11:06:10.971361Z

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Observation dedfef61-3138-4032-b957-759c42a0ff94 · outbound

This paper cites keywords =.

A machine learning approach to estimating HI deficiency in galaxies keywords =

Reference 80

Resolution: verified exact; doi, observed 2026-07-09T11:06:10.964427Z

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Observation 1a757816-0b9c-4dc7-bf61-0d8714640585 · outbound

This paper cites Faint and fading tails : the fate of stripped HI gas in Virgo cluster galaxies.

A machine learning approach to estimating HI deficiency in galaxies Faint and fading tails : the fate of stripped HI gas in Virgo cluster galaxies

Reference 81

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:11.023230Z

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Observation e0182a39-0ce7-49e8-bb1c-bb4c94dcef4f · outbound

This paper cites Pattern recognition in the ALFALFA.70 and Sloan Digital Sky Surveys: A catalog of $\sim$ 500,000 HI gas fraction estimates based on artificial neural networks.

A machine learning approach to estimating HI deficiency in galaxies Pattern recognition in the ALFALFA.70 and Sloan Digital Sky Surveys: A catalog of $\sim$ 500,000 HI gas fraction estimates based on artificial neural networks

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.994304Z

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Observation e31812f9-9004-47a2-8685-842bfca6bfa0 · outbound

This paper cites Connecting optical morphology, environment, and HI mass fraction for low-redshift galaxies using deep learning.

A machine learning approach to estimating HI deficiency in galaxies Connecting optical morphology, environment, and HI mass fraction for low-redshift galaxies using deep learning

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-07-09T11:06:10.887725Z

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Observation 089c9c27-dbc9-48b0-a3d8-20e6773ba353 · outbound

This paper cites doi:10.5281/zenodo.19711065 , url =.

A machine learning approach to estimating HI deficiency in galaxies doi:10.5281/zenodo.19711065 , url =

Reference 84

Resolution: verified exact; doi, observed 2026-07-09T11:06:10.984387Z

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Observation 231ec9d2-11d7-416a-9be5-a26910e9b08d · outbound

This paper cites doi:10.5281/zenodo.19819331 , url =.

A machine learning approach to estimating HI deficiency in galaxies doi:10.5281/zenodo.19819331 , url =

Reference 85

Resolution: verified exact; doi, observed 2026-07-09T11:06:11.016585Z

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