Paper Citation Record · LEDGER

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation

As of 23 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2508.19574.

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

pith.paper-citation-record.v1
2508.19574 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:45:45.456476Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:31:15.372628Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T07:20:57.857258Z

Reference resolution

40 of 40 outbound references displayed

verified exact2
verified fuzzy28
unresolved10
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65575e6b-7a92-4df0-b909-119025623a64 · outbound

This paper cites Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:50.354743Z

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Observation 341e0b27-0615-4691-9ff1-301a9196b0cf · outbound

This paper cites Multi-scale domain-adversarial multiple-instance cnn for cancer subtype classifica- tion with unannotated histopathological images,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Multi-scale domain-adversarial multiple-instance cnn for cancer subtype classifica- tion with unannotated histopathological images,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:50.231247Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e13b4eb2-d177-4b0b-9616-ce2e54aa7071 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Fully convolutional networks for semantic segmentation,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T15:45:40.392451Z

Source-reported events for the cited work

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Observation 216f4ecc-89d4-448e-ad12-2720f13f1d0b · outbound

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

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T15:45:40.554751Z

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Observation e107d6b6-2e27-4ceb-b10b-79e5c9e6972d · outbound

This paper cites Encoder- decoder with atrous separable convolution for semantic image segmen- tation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T15:45:40.692193Z

Source-reported events for the cited work

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Observation f067d45a-289d-4799-b401-421c029ccfeb · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T15:45:40.869138Z

Source-reported events for the cited work

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Observation 1c175c7b-7e33-4a92-a520-d6f497fdf75b · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmenta- tion,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmenta- tion,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T15:45:41.002252Z

Source-reported events for the cited work

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Observation 35124a0a-a875-4328-9be3-96e1f37999a2 · outbound

This paper cites Clinical-grade computational pathology using weakly supervised deep learning on whole slide images,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Clinical-grade computational pathology using weakly supervised deep learning on whole slide images,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:49.698786Z

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Observation f8934d89-b323-4db9-948c-ba4a0c860735 · outbound

This paper cites Deep semi-supervised learning for medical image segmentation: A review,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Deep semi-supervised learning for medical image segmentation: A review,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:49.641210Z

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Observation 6a912148-079e-4e2e-8cd0-81d0f602544f · outbound

This paper cites Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:49.505286Z

Source-reported events for the cited work

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Observation 8c887980-8816-4485-929a-716907ca3879 · outbound

This paper cites Adversarial Learning for Semi-Supervised Semantic Segmentation.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Adversarial Learning for Semi-Supervised Semantic Segmentation

Reference 11

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Source-reported events for the cited work

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Observation be78487f-961c-4a79-898e-c7b1a2c8123e · outbound

This paper cites Billion-scale semi-supervised learning for image classification.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Billion-scale semi-supervised learning for image classification

Reference 12

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Source-reported events for the cited work

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Observation 29f59201-b044-4a3c-ac06-4104482afac1 · outbound

This paper cites Fixmatch: Simplifying semi- supervised learning with consistency and confidence,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Fixmatch: Simplifying semi- supervised learning with consistency and confidence,

Reference 13

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Source-reported events for the cited work

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Observation 341cb553-203e-479f-8e73-b8bac5314fd7 · outbound

This paper cites Revisiting weak- to-strong consistency in semi-supervised semantic segmentation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Revisiting weak- to-strong consistency in semi-supervised semantic segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:49.420050Z

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Observation a87bf01e-580f-4fda-a80b-cd77c0babd5a · outbound

This paper cites Clims: Cross language image matching for weakly supervised semantic segmentation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Clims: Cross language image matching for weakly supervised semantic segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:49.323711Z

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Observation 023486d8-acb6-497c-9905-c446376e84f1 · outbound

This paper cites Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:49.234745Z

Source-reported events for the cited work

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Observation f24648dd-4a3a-4808-8dcc-11468c6c5363 · outbound

This paper cites Pamil: Prototype attention-based multiple instance learning for whole slide image classification,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Pamil: Prototype attention-based multiple instance learning for whole slide image classification,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:49.074802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8d62a2d6-35f2-48ae-b57f-34900921df68 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Learning transferable visual models from natural language supervision,

Reference 18

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Source-reported events for the cited work

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Observation 551c9596-6283-4c3e-83a3-4623461251c7 · outbound

This paper cites A visual–language foundation model for pathology image analysis using medical twitter,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation A visual–language foundation model for pathology image analysis using medical twitter,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:48.959591Z

Source-reported events for the cited work

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Observation 5a5d6df8-f6f2-420d-a197-f4dbfd82cd6d · outbound

This paper cites A visual-language foundation model for computational pathology,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation A visual-language foundation model for computational pathology,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:48.824821Z

Source-reported events for the cited work

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Observation d874982b-6775-42d5-9fde-079f94f26c4b · outbound

This paper cites Learning to prompt for vision- language models,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Learning to prompt for vision- language models,

Reference 21

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Source-reported events for the cited work

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Observation 95a4c2a7-1775-4976-bba4-087be5a5ecf7 · outbound

This paper cites Towards a general-purpose foundation model for computational pathology,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Towards a general-purpose foundation model for computational pathology,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:48.604746Z

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Observation 595205a4-1c1d-4a5d-b871-1bf893f902c7 · outbound

This paper cites Semi-supervised deep transfer learn- ing for benign-malignant diagnosis of pulmonary nodules in chest ct images,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Semi-supervised deep transfer learn- ing for benign-malignant diagnosis of pulmonary nodules in chest ct images,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:48.464879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation facb9f78-4f18-4ea5-9a03-beb8db0766d8 · outbound

This paper cites Semixup: In- and out-of-manifold regularization for deep semi-supervised knee os- teoarthritis severity grading from plain radiographs,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Semixup: In- and out-of-manifold regularization for deep semi-supervised knee os- teoarthritis severity grading from plain radiographs,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:48.316523Z

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Observation 8e5fb7d1-78c7-44a2-8f9a-5981c2b311f7 · outbound

This paper cites Shadow-consistent semi- supervised learning for prostate ultrasound segmentation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Shadow-consistent semi- supervised learning for prostate ultrasound segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:48.195397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 17104c6f-1399-4e08-9721-073a44e6ffe1 · outbound

This paper cites Few-shot learning by a cascaded framework with shape-constrained pseudo label assessment for whole heart segmentation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Few-shot learning by a cascaded framework with shape-constrained pseudo label assessment for whole heart segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:48.025532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation df852b41-2a97-428f-b0ea-f39520c782d3 · outbound

This paper cites Semantic segmen- tation with generative models: Semi-supervised learning and strong out- of-domain generalization,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Semantic segmen- tation with generative models: Semi-supervised learning and strong out- of-domain generalization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:47.954954Z

Source-reported events for the cited work

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Observation 4fea5a03-9a4c-4714-a82a-c30b8c93892a · outbound

This paper cites Revisiting consistency regularization for semi-supervised learning,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Revisiting consistency regularization for semi-supervised learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:47.824745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 03982538-953c-4fcc-b6e1-888d2825d456 · outbound

This paper cites Semi-supervised deep learning via transformation consistency regular- ization for remote sensing image semantic segmentation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Semi-supervised deep learning via transformation consistency regular- ization for remote sensing image semantic segmentation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:47.694748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9107c137-6289-4653-9248-833ff78f748a · outbound

This paper cites Semi- supervised medical image segmentation using adversarial consistency learning and dynamic convolution network,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Semi- supervised medical image segmentation using adversarial consistency learning and dynamic convolution network,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:47.561835Z

Source-reported events for the cited work

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Observation cfd1550c-7cd0-473c-b82d-b59cc8f6bde1 · outbound

This paper cites A three-stage self-training framework for semi-supervised semantic segmentation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation A three-stage self-training framework for semi-supervised semantic segmentation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:47.414751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 94f4648f-cbc6-4444-ac83-b4938f8261e1 · outbound

This paper cites Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:47.242790Z

Source-reported events for the cited work

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Observation b2bd487a-faeb-40ed-a37b-fd0f180bbfe5 · outbound

This paper cites A semi-supervised learning approach for tissue semantic segmentation in whole slide images,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation A semi-supervised learning approach for tissue semantic segmentation in whole slide images,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:46.967089Z

Source-reported events for the cited work

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Observation c1405d5e-37f3-40a5-aafa-b42d8007427c · outbound

This paper cites Semi-supervised breast cancer pathology image segmentation based on fine-grained clas- sification guidance,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Semi-supervised breast cancer pathology image segmentation based on fine-grained clas- sification guidance,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:46.684731Z

Source-reported events for the cited work

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Observation 602b6ecb-7eb4-4da5-96bc-50a7087f5e8e · outbound

This paper cites Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:46.514801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e29e3f5e-9ddf-4f8f-9d6f-b5aa3d98cd5e · outbound

This paper cites A semi-supervised learning for segmentation of gigapixel histopathol- ogy images from brain tissues,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation A semi-supervised learning for segmentation of gigapixel histopathol- ogy images from brain tissues,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:46.364749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c9132d4c-7b50-4dcc-8150-5f9bb2e2ba62 · outbound

This paper cites Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:45:45.783435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 735f0b9e-3deb-4d8e-940c-20f35d2b9781 · outbound

This paper cites mTREE: Multi-Level Text-Guided Representation End-to-End Learning for Whole Slide Image Analysis.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation mTREE: Multi-Level Text-Guided Representation End-to-End Learning for Whole Slide Image Analysis

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:45:45.707335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6badab83-d744-4c5b-9876-c2256b8a4029 · outbound

This paper cites Pathology-knowledge enhanced multi-instance prompt learning for few- shot whole slide image classification,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Pathology-knowledge enhanced multi-instance prompt learning for few- shot whole slide image classification,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:46.235802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7276ea22-81ee-4393-907d-b8a2c22f5fed · outbound

This paper cites Unet 3+: A full-scale connected unet for medical image segmentation,.

Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation Unet 3+: A full-scale connected unet for medical image segmentation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:45:46.147221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 5923bef4-17fe-46fb-9a0e-33551c213b09 · inbound

UniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentation cites this paper.

UniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentation Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:20:57.859254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 77792702-a2f6-48c5-be74-c61e6a953fb3 · inbound

ProBAG: Prototype-Guided Boundary-Aware Graph Diffusion for Weakly Supervised Histopathology Segmentation cites this paper.

ProBAG: Prototype-Guided Boundary-Aware Graph Diffusion for Weakly Supervised Histopathology Segmentation Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation

Reference 9

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Source-reported events for the cited work

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