Paper Citation Record · LEDGER

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis

As of 4 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2604.20928.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2604.20928 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T00:25:18.564814Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact2
verified fuzzy38
unresolved0
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c181d8d4-a7a3-424f-b8e5-124097b601d9 · outbound

This paper cites A novel deep denoising model integrating transformer and time–frequency loss for gearbox fault diagnosis.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis A novel deep denoising model integrating transformer and time–frequency loss for gearbox fault diagnosis

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.734776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eeb50e86-d2ee-4420-8d75-1ab628cc8332 · outbound

This paper cites VibrMamba: A lightweight Mamba-based fault diagnosis of rotating machinery using vibration signal.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis VibrMamba: A lightweight Mamba-based fault diagnosis of rotating machinery using vibration signal

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.759735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 661b260a-9fb1-4524-919a-a4af4e2e3e6d · outbound

This paper cites Large language models for fault diagnosis.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Large language models for fault diagnosis

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.845873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d41ee0fc-37ca-457f-8bec-b402742f7cd1 · outbound

This paper cites Deep learning.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Deep learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.739699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 39c7f387-ca94-42b0-953e-4eb2282de6a9 · outbound

This paper cites Review of intelligent fault diagnosis for rotating machinery under imperfect data conditions.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Review of intelligent fault diagnosis for rotating machinery under imperfect data conditions

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.726708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dce70a8e-a57a-4648-9948-92e135c97f24 · outbound

This paper cites Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.743604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 555599ac-47d8-4941-a6ea-af45508f42ae · outbound

This paper cites Fault diagnosis in rotating machines based on transfer learning: Literature review.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Fault diagnosis in rotating machines based on transfer learning: Literature review

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.803126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d6058c76-0d70-4a93-9b56-be0bd2718047 · outbound

This paper cites Progressive transfer learning: An intelligent fault diagnosis method for unlabeled rotating machinery with small samples.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Progressive transfer learning: An intelligent fault diagnosis method for unlabeled rotating machinery with small samples

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.779204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ed55cf0f-224e-43f3-b94a-19ddae509d89 · outbound

This paper cites Universal domain adaptation in rotating machinery fault diagnosis: A self-supervised orthogonal clustering approach.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Universal domain adaptation in rotating machinery fault diagnosis: A self-supervised orthogonal clustering approach

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.751630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9ef564d3-bdb3-46d5-8258-129cfe523cce · outbound

This paper cites Global-focal adaptation with information separation for noise-robust transfer fault diagnosis.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Global-focal adaptation with information separation for noise-robust transfer fault diagnosis

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:29:47.522880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f00b545f-b183-421d-ba05-3c5ec6bed0b2 · outbound

This paper cites Enhancing bear- ing fault diagnosis in real damages: A hybrid multi-domain generalization network for feature comparison.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Enhancing bear- ing fault diagnosis in real damages: A hybrid multi-domain generalization network for feature comparison

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.755600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 83762afb-8057-4b5c-800a-ac9759123760 · outbound

This paper cites Domain generalization for rotating machinery fault diagnosis: A survey.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Domain generalization for rotating machinery fault diagnosis: A survey

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.853675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3b505afd-634f-4deb-900f-d8f461d98c3d · outbound

This paper cites A two-stage semi- supervised domain generalization network for fault diagnosis under unknown working conditions.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis A two-stage semi- supervised domain generalization network for fault diagnosis under unknown working conditions

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.747669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 421f6d7e-06c0-4c06-9fbe-827487b31834 · outbound

This paper cites Semi-supervised dynamic generalization network with dual feature enhancement strat- egy for machinery fault diagnosis under unseen working conditions.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Semi-supervised dynamic generalization network with dual feature enhancement strat- egy for machinery fault diagnosis under unseen working conditions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.807617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 37bd23fa-6dc6-4f56-86ed-9b915b5bc696 · outbound

This paper cites Deep semisupervised domain generalization network for rotary machinery fault diagnosis under variable speed.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Deep semisupervised domain generalization network for rotary machinery fault diagnosis under variable speed

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.730946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0d6b531e-fd9a-4692-a538-6077cf73e38c · outbound

This paper cites Domain fuzzy generalization networks for semi-supervised intelligent fault diagnosis under unseen working conditions.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Domain fuzzy generalization networks for semi-supervised intelligent fault diagnosis under unseen working conditions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.722607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9eacd77d-797d-4dab-86df-da42e0815b99 · outbound

This paper cites Mutual-assistance semisupervised domain generalization network for intelligent fault diagnosis under unseen working conditions.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Mutual-assistance semisupervised domain generalization network for intelligent fault diagnosis under unseen working conditions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.819140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 24e55bfc-4f80-417a-9a1d-411812d29a94 · outbound

This paper cites Contrast-assisted domain- specificity-removal network for semi-supervised generalization fault diagnosis.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Contrast-assisted domain- specificity-removal network for semi-supervised generalization fault diagnosis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.766489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 20910f94-0961-4fd4-a1e7-4b8c5270c722 · outbound

This paper cites Semi-supervised domain general- ization with clustering and contrastive learning combined mechanism.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Semi-supervised domain general- ization with clustering and contrastive learning combined mechanism

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.717939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation af89015e-61a6-4bdb-af35-7c7a9eee7062 · outbound

This paper cites Domain knowledge guided pseudo-label generation framework for semi-supervised domain generalization fault diagnosis.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Domain knowledge guided pseudo-label generation framework for semi-supervised domain generalization fault diagnosis

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.858245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4b7f8422-ce07-4e5d-be63-a8a35a3cadcb · outbound

This paper cites A noise-resilient fault diagnosis method based on optimized residual networks.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis A noise-resilient fault diagnosis method based on optimized residual networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.842209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5ac2b3d1-57ec-47bd-a9f6-de4ed811fc62 · outbound

This paper cites UDDGN: Domain- independent compact boundary learning method for universal diagnosis domain generation.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis UDDGN: Domain- independent compact boundary learning method for universal diagnosis domain generation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.822817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5d7c1409-1a79-485f-89f2-d16f7559ce50 · outbound

This paper cites Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.775215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c1bb861-6bfb-4c1d-9421-2daca3c08a1b · outbound

This paper cites Domain generalization network based on inter-domain multivariate linearization for intelligent fault diagnosis.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Domain generalization network based on inter-domain multivariate linearization for intelligent fault diagnosis

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.811176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 29e935ab-ffa6-4e1b-b89a-1091111ae789 · outbound

This paper cites Multiple classifiers inconsistency-based deep adversarial domain generalization method for cross-condition fault diagnosis in rotating systems.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Multiple classifiers inconsistency-based deep adversarial domain generalization method for cross-condition fault diagnosis in rotating systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.827105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4205f6c0-32ff-4271-9f0a-708d02fb774f · outbound

This paper cites A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.783076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f53d498d-014a-4a5a-8d4d-c599dee013b5 · outbound

This paper cites Causality-inspired multi- source domain generalization method for intelligent fault diagnosis under unknown operating conditions.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Causality-inspired multi- source domain generalization method for intelligent fault diagnosis under unknown operating conditions

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.763359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f138dca6-16f3-4d1e-976a-845ff43c538b · outbound

This paper cites Meta-learning based domain generaliza- tion framework for fault diagnosis with gradient aligning and semantic matching.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Meta-learning based domain generaliza- tion framework for fault diagnosis with gradient aligning and semantic matching

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.795054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9aafc4e9-f678-4dda-94bc-d390dfc93d8a · outbound

This paper cites Domain-augmented meta ensemble learning for mechanical fault diagnosis from heteroge- neous source domains to unseen target domains.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Domain-augmented meta ensemble learning for mechanical fault diagnosis from heteroge- neous source domains to unseen target domains

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.838753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ce1e9ca9-2bb2-450d-81da-a924f08e69b0 · outbound

This paper cites A new adversarial domain generalization network based on class boundary feature detection for bearing fault diagnosis.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis A new adversarial domain generalization network based on class boundary feature detection for bearing fault diagnosis

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.791507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eed35f8e-3597-4f38-8a9c-8163c96da804 · outbound

This paper cites Domain-invariant feature fusion networks for semi-supervised generalization fault diag- nosis.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Domain-invariant feature fusion networks for semi-supervised generalization fault diag- nosis

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.831028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4d326522-c59b-4b5a-a3e1-45f3caaf23c8 · outbound

This paper cites Domain-adversarial training of neural networks.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Domain-adversarial training of neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.799329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6a13c875-c077-4dd9-9a2b-d690b52fc2bc · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Representation Learning with Contrastive Predictive Coding

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-10T00:29:47.525338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 57942b63-1b2e-4408-a85a-ac802591b8a5 · outbound

This paper cites Fuzzy sets.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Fuzzy sets

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.712158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 975b1a9c-1537-4b7b-bc05-2c811b125599 · outbound

This paper cites Rolling element bearing diagnostics using the case western reserve university data: A benchmark study.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Rolling element bearing diagnostics using the case western reserve university data: A benchmark study

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.815014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c6f9267b-b840-4788-a682-02f40500eecf · outbound

This paper cites Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.787905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a8ee4f0b-7d07-4027-851c-b1955f74aa8f · outbound

This paper cites Study of JUST slewing bearing failure test data.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Study of JUST slewing bearing failure test data

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.849836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 011d97e5-4494-4ac3-93af-0cc3e7b8a2bd · outbound

This paper cites Rotating machinery fault-induced vibration signal modulation effects: A review with mecha- nisms, extraction methods and applications for diagnosis.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Rotating machinery fault-induced vibration signal modulation effects: A review with mecha- nisms, extraction methods and applications for diagnosis

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.862193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 54f99753-a0d7-41f2-94dd-de4f6722fb32 · outbound

This paper cites ConvNeXt V2: Co-designing and scaling convnets with masked autoencoders.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis ConvNeXt V2: Co-designing and scaling convnets with masked autoencoders

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.834581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1b1ecb16-99a8-4f7c-b602-ff378942d147 · outbound

This paper cites Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation.

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:22:53.770749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

No inbound Pith citation observations are available.