Paper Citation Record · LEDGER

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking

As of 10 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2502.03907.

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

pith.paper-citation-record.v1
2502.03907 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:16:55.190000Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:16:55.046185Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T00:16:55.280901Z

Reference resolution

32 of 32 outbound references displayed

verified exact1
verified fuzzy23
unresolved8
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External citation measurements

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Outbound references

Observation 01421223-13ca-4d59-acac-b4a1da62f210 · outbound

This paper cites To build these robust track- ing models, a significant amount of annotated data is required.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking To build these robust track- ing models, a significant amount of annotated data is required

Reference 1

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Observation b3358071-89dd-4fb7-9941-0c49e30a6db9 · outbound

This paper cites No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking

Reference 2

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Observation ad620be2-6a3b-4915-8ac0-cb6734d30a8e · outbound

This paper cites an unresolved cited work.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Unresolved cited work

Reference 3

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Observation 8d5cc5a6-58f8-4c09-9795-5b7fc8b1d56d · outbound

This paper cites The criterion is not met if AX /∈ [(1 − α)AY , (1 +α)AY ].

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking The criterion is not met if AX /∈ [(1 − α)AY , (1 +α)AY ]

Reference 4

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Observation 39f93ac8-c942-4b10-b24b-8c430f2183ce · outbound

This paper cites Per- Clustering (optional) (a) Original (b) Logits (d) Semantic masks (c) Seed regions (e) Watershed Fig.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Per- Clustering (optional) (a) Original (b) Logits (d) Semantic masks (c) Seed regions (e) Watershed Fig

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:16:55.675458Z

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Observation 3bdd757d-27f7-48dd-8e4d-2cda90cfe5ec · outbound

This paper cites an unresolved cited work.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Unresolved cited work

Reference 6

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Observation 05bc9be5-c673-4fa6-b1cf-ffc61e84cdc0 · outbound

This paper cites Inconsistent bounding boxes can lead to suboptimal model performance due to noise.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Inconsistent bounding boxes can lead to suboptimal model performance due to noise

Reference 7

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Observation b5fdafa5-ef96-4841-a4b6-3f0e658cb7e4 · outbound

This paper cites In SAM-QA, prompts are not set at fixed intervals but are triggered by conflict occurrences, enhancing accuracy.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking In SAM-QA, prompts are not set at fixed intervals but are triggered by conflict occurrences, enhancing accuracy

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:16:55.660298Z

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Observation 1e0dcb8d-0eaa-40d7-a386-89adc6acd024 · outbound

This paper cites The recordings were sourced from previous studies, ensuring no additional impact on the animals during this work.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking The recordings were sourced from previous studies, ensuring no additional impact on the animals during this work

Reference 9

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Observation 245e2d3d-b11f-4558-b672-30fbf57db313 · outbound

This paper cites Animal Welfare: Severity Assessment in Experimen- tal Research,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Animal Welfare: Severity Assessment in Experimen- tal Research,

Reference 10

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Observation 05248c30-d4d8-4432-9ccc-11424e1818ec · outbound

This paper cites Multi-animal pose estimation, iden- tification and tracking with DeepLabCut,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Multi-animal pose estimation, iden- tification and tracking with DeepLabCut,

Reference 11

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Observation f22f6d7f-1536-42a1-a32b-f65d6012e26a · outbound

This paper cites Segment anything,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Segment anything,

Reference 12

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Observation 21555fa3-131a-4d6b-988c-f04cdf052fca · outbound

This paper cites Segment anything for mi- croscopy,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Segment anything for mi- croscopy,

Reference 13

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Observation ea579f47-203e-48ff-91c4-35ae129abdd3 · outbound

This paper cites Segment Anything Meets Point Tracking.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Segment Anything Meets Point Tracking

Reference 14

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Observation 71647153-907b-4c26-be51-1a5e52a98101 · outbound

This paper cites CoTracker: It is better to track together,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking CoTracker: It is better to track together,

Reference 15

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Observation 8edc726d-d69f-4e81-8d9a-a80c7d0ef3da · outbound

This paper cites PointOdyssey: A large-scale synthetic dataset for long-term point track- ing,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking PointOdyssey: A large-scale synthetic dataset for long-term point track- ing,

Reference 16

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Observation 46335c94-107d-49db-9fd6-da040907cdbd · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking SAM 2: Segment Anything in Images and Videos

Reference 17

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This paper cites TinyViT: Fast pretraining distillation for small vision transformers,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking TinyViT: Fast pretraining distillation for small vision transformers,

Reference 18

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Observation 3b1d271a-0638-4505-9ba2-f256809c1162 · outbound

This paper cites Generalised dice overlap as a deep learning loss function for highly un- balanced segmentations,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Generalised dice overlap as a deep learning loss function for highly un- balanced segmentations,

Reference 19

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Observation fc78c480-6acc-42fd-8712-837d9316e93a · outbound

This paper cites an unresolved cited work.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Unresolved cited work

Reference 20

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Observation 0d7bb0cc-0f66-4550-b801-8280c3529767 · outbound

This paper cites an unresolved cited work.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Unresolved cited work

Reference 21

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Observation 8ce57a2d-4644-44c0-9cce-4980f3d2644b · outbound

This paper cites U-Net: Convolutional networks for biomedical image segmentation,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking U-Net: Convolutional networks for biomedical image segmentation,

Reference 22

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Observation 1deefa1a-45f7-4785-a9c2-af76327bfd9a · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 23

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Observation ce24c3af-7685-42b4-b495-6369074fd41e · outbound

This paper cites DINOv2: Learning robust visual features without supervision,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking DINOv2: Learning robust visual features without supervision,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:16:55.427407Z

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This paper cites Use of watersheds in contour detec- tion,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Use of watersheds in contour detec- tion,

Reference 25

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This paper cites Least squares quantization in PCM,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Least squares quantization in PCM,

Reference 26

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Observation 1f8359f1-1881-402e-b971-9f950fb261aa · outbound

This paper cites Maximum likelihood from incomplete data via the EM algorithm,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Maximum likelihood from incomplete data via the EM algorithm,

Reference 27

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Observation e3bc9359-cece-4381-a50c-01a551a200ca · outbound

This paper cites Grounding DINO: Marrying DINO with grounded pre-training for open-set object detec- tion,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Grounding DINO: Marrying DINO with grounded pre-training for open-set object detec- tion,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:16:55.364813Z

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Observation 843eabc8-ccca-4567-b2c6-bd56090c242b · outbound

This paper cites ByteTrack: Multi-object tracking by associating every detection box,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking ByteTrack: Multi-object tracking by associating every detection box,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:16:55.348893Z

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Observation 2a982fb7-fe91-4495-a79f-1d1e3f9236b8 · outbound

This paper cites Ultra- lytics yolov8,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Ultra- lytics yolov8,

Reference 30

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Observation 6385ad0e-e67e-4047-be3e-0959d328ebf1 · outbound

This paper cites Performance measures and a data set for multi-target, multi-camera tracking,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Performance measures and a data set for multi-target, multi-camera tracking,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:16:55.321301Z

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Observation 3e470ac9-b211-4ecf-b5fc-3eee01dc8fd4 · outbound

This paper cites HOTA: A higher order metric for evaluating multi-object tracking,.

No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking HOTA: A higher order metric for evaluating multi-object tracking,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:16:55.304819Z

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Pith citing papers

Observation b3358071-89dd-4fb7-9941-0c49e30a6db9 · inbound