Paper Citation Record · LEDGER

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets

As of 13 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2412.03452.

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

pith.paper-citation-record.v1
2412.03452 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:26:49.330230Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

34 of 34 outbound references displayed

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verified fuzzy30
unresolved2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bf52aa4b-cdc2-488d-a06b-977e690b8283 · outbound

This paper cites write newline.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets write newline

Reference 1

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Source-reported events for the cited work

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Observation 6b3600d0-91dd-4539-b0eb-b6f937495b62 · outbound

This paper cites A systematic review of rare events detection across modalities using machine learning and deep learning.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets A systematic review of rare events detection across modalities using machine learning and deep learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.812391Z

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Observation 58372550-bffb-4edb-bb26-7122eb7441fc · outbound

This paper cites Ubnormal: New benchmark for supervised open-set video anomaly detection.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Ubnormal: New benchmark for supervised open-set video anomaly detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.798854Z

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Observation 43c5e9a4-cf62-4968-ac4a-f0f94b6514cc · outbound

This paper cites Assessing the determinants of larval fish strike rates using computer vision.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Assessing the determinants of larval fish strike rates using computer vision

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.785049Z

Source-reported events for the cited work

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Observation 26eb9409-0cac-4627-b71c-d8f5c9dd7823 · outbound

This paper cites Mvtec ad--a comprehensive real-world dataset for unsupervised anomaly detection.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Mvtec ad--a comprehensive real-world dataset for unsupervised anomaly detection

Reference 5

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Observation 4930ada5-caf7-4c90-8445-2d91259e41c2 · outbound

This paper cites On model evaluation under non-constant class imbalance.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets On model evaluation under non-constant class imbalance

Reference 6

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Observation 53a99c06-53fc-49a1-aab2-9fde47e8f1a2 · outbound

This paper cites A novel biomechanical approach for animal behaviour recognition using accelerometers.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets A novel biomechanical approach for animal behaviour recognition using accelerometers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.746895Z

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Observation 5aa1d98f-7aa0-4a06-9904-3dcb8bf6f3d7 · outbound

This paper cites The philosophy of outliers: Reintegrating rare events into biological science.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets The philosophy of outliers: Reintegrating rare events into biological science

Reference 8

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Observation d8476c03-641b-459e-bc14-88576dd0ceb6 · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets NICE: Non-linear Independent Components Estimation

Reference 9

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Source-reported events for the cited work

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Observation 642a1198-7cf9-4099-bf38-f8652e8fb4a1 · outbound

This paper cites Improving the precision of estimates of the frequency of rare events.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Improving the precision of estimates of the frequency of rare events

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.718543Z

Source-reported events for the cited work

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Observation b37b90db-0e3d-48a3-a4ae-2d9bdd56efb6 · outbound

This paper cites Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.704811Z

Source-reported events for the cited work

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Observation 5105f505-0887-43e1-863a-e3113bb04892 · outbound

This paper cites Normalizing flows for human pose anomaly detection.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Normalizing flows for human pose anomaly detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.690929Z

Source-reported events for the cited work

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Observation 43285505-195f-400a-9b59-a114b2458bfe · outbound

This paper cites Surface defect saliency of magnetic tile.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Surface defect saliency of magnetic tile

Reference 13

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Observation 56480691-8357-4050-95e2-c34dcc6bc158 · outbound

This paper cites Probabilistic models of larval zebrafish behavior reveal structure on many scales.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Probabilistic models of larval zebrafish behavior reveal structure on many scales

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.662004Z

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Observation 2e221165-45c8-4a55-948b-12c64f9157a9 · outbound

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Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Unresolved cited work

Reference 15

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Observation 1efc9c39-0001-4d82-85d2-2c4e58b7ce61 · outbound

This paper cites Coarse-to-fine animal pose and shape estimation.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Coarse-to-fine animal pose and shape estimation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.634104Z

Source-reported events for the cited work

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Observation 6ed8e261-b59d-4661-99ba-6f0fbc2f1dc1 · outbound

This paper cites Future frame prediction for anomaly detection--a new baseline.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Future frame prediction for anomaly detection--a new baseline

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.620263Z

Source-reported events for the cited work

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Observation 4a0bef34-c832-49ba-bb06-bacc116e7ff8 · outbound

This paper cites Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.607292Z

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Observation 6b227008-f623-4e94-a976-12630e024181 · outbound

This paper cites Mark, Deva Ramanan, and Kayvon Fatahalian.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Mark, Deva Ramanan, and Kayvon Fatahalian

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.594438Z

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Observation 1e5f58cf-abde-4ece-a224-28bc6ad736a5 · outbound

This paper cites Poser-a deep learning toolbox for decoding animal behavior.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Poser-a deep learning toolbox for decoding animal behavior

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.581054Z

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Observation c2ad685a-b758-4044-ade4-3de19dcfe730 · outbound

This paper cites Simple behavioral analysis (simba)--an open source toolkit for computer classification of complex social behaviors in experimental animals.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Simple behavioral analysis (simba)--an open source toolkit for computer classification of complex social behaviors in experimental animals

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.566480Z

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Observation cdc2aec2-200e-41d4-b6e1-12c74309ec7c · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Pytorch: An imperative style, high-performance deep learning library

Reference 22

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Observation 2e9e8f6f-10c2-4190-8fb3-ef290437e83f · outbound

This paper cites Active learning for anomaly and rare-category detection.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Active learning for anomaly and rare-category detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.538055Z

Source-reported events for the cited work

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Observation 8d5f2bf4-1e3a-4eac-a25e-ad6f2902506e · outbound

This paper cites The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.524760Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d582c962-375c-46d4-a5ed-9675e6ffad10 · outbound

This paper cites Precrec: fast and accurate precision--recall and roc curve calculations in r.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Precrec: fast and accurate precision--recall and roc curve calculations in r

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.510647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5c532f75-81fe-427f-a361-bd845c0beb6e · outbound

This paper cites Real-world anomaly detection in surveillance videos.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Real-world anomaly detection in surveillance videos

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.496779Z

Source-reported events for the cited work

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Observation ce31d949-86e5-4db8-80b0-0db62a27ae10 · outbound

This paper cites Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.483045Z

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Observation c2e4ad58-87a5-4d36-8ce8-aaeabcd3c008 · outbound

This paper cites Perspectives in machine learning for wildlife conservation.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Perspectives in machine learning for wildlife conservation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.468966Z

Source-reported events for the cited work

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Observation eb0d9f04-8afe-47b1-98d2-038fbe5194b5 · outbound

This paper cites Keypoint-moseq: parsing behavior by linking point tracking to pose dynamics.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Keypoint-moseq: parsing behavior by linking point tracking to pose dynamics

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.454882Z

Source-reported events for the cited work

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This paper cites Daniel Salzman, Dora Angelaki, Andr\' e s Bendesky, The International Brain Laboratory The International Brain Laboratory, John P Cunningham, and Liam Paninski.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Daniel Salzman, Dora Angelaki, Andr\' e s Bendesky, The International Brain Laboratory The International Brain Laboratory, John P Cunningham, and Liam Paninski

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.440301Z

Source-reported events for the cited work

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Observation 32efea9d-286e-47bd-baf9-0f955bb0f51b · outbound

This paper cites Spatial temporal graph convolutional networks for skeleton-based action recognition.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Spatial temporal graph convolutional networks for skeleton-based action recognition

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.425171Z

Source-reported events for the cited work

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Observation 5f77c93a-9fdf-4b71-b63a-4d8d52b5e59c · outbound

This paper cites Graph regularized flow attention network for video animal counting from drones.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Graph regularized flow attention network for video animal counting from drones

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.410802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9f7b18be-976e-4ffa-a2bf-82d6122378e0 · outbound

This paper cites Visualization of regression models using visreg.

Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Visualization of regression models using visreg

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:49.396720Z

Source-reported events for the cited work

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Observation c8839ff5-be17-408b-aeb3-5b29a0a376e7 · outbound

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Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets Unresolved cited work

Reference 34

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.