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Paper Citation Record · LEDGER

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks

As of 21 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2508.20829.

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

pith.paper-citation-record.v1
2508.20829 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:51:12.566984Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

35 of 35 outbound references displayed

  • verified exact2
  • verified fuzzy25
  • unresolved8
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0f2fad05-0652-48c1-b9be-dbafff4a3614 · outbound

This paper cites Blockchain: A Graph Primer.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Blockchain: A Graph Primer

Reference 1

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local_arxiv, observed 2026-08-05T14:51:12.634245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 2390d72a-105c-41ea-be31-250e7fdfdfd9 · outbound

This paper cites Data mining for credit card fraud: A comparative study.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Data mining for credit card fraud: A comparative study

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation a31f5d6f-dffa-405a-981f-a9d44d0154fe · outbound

This paper cites Random forests.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Random forests

Reference 3

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Observation 87262386-6544-459d-87b0-3a5220e30345 · outbound

This paper cites XBLOCK Blockchain Datasets: InPlusLab ethereum phishing detection datasets.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks XBLOCK Blockchain Datasets: InPlusLab ethereum phishing detection datasets

Reference 4

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raw_fallback, observed 2026-08-05T14:51:12.840645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 7737adca-d4fc-4252-9b83-4bdd6c3c9853 · outbound

This paper cites Xgboost: A scalable tree boosting system.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Xgboost: A scalable tree boosting system

Reference 5

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Observation a31766c5-deef-4698-a24c-1833af5b366f · outbound

This paper cites Understanding ethereum via graph analysis.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Understanding ethereum via graph analysis

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 77e48ded-7d19-4dd7-85ba-c1a6e0bc09a5 · outbound

This paper cites Motif graph neural network.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Motif graph neural network

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:51:12.487038Z digest=sha256:aaa52df1070fc14887fa9b75cd5304a9522be87651890ca11b58d6ece884edb5

Observation fb5598cd-d58b-409c-bc0c-6eddb6627317 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Generating Long Sequences with Sparse Transformers

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:51:12.489699Z digest=sha256:60bc7846fad089319b3bf4974bf8783b16753c9edab54e13e0f8554c7ca865c0

Observation ceedd5b9-5d23-441b-879b-ac8a17d1344b · outbound

This paper cites Graph anomaly detection via multi-scale contrastive learning networks with augmented view.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Graph anomaly detection via multi-scale contrastive learning networks with augmented view

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-05T14:51:12.813491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 0bb5173a-af60-4537-b327-c5c126063905 · outbound

This paper cites Arise: Graph anomaly detection on attributed networks via substructure awareness.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Arise: Graph anomaly detection on attributed networks via substructure awareness

Reference 10

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raw_fallback, observed 2026-08-05T14:51:12.805667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation fbff2765-c97d-4722-8ad1-e706ea968d7c · outbound

This paper cites Provably powerful graph neural networks for directed multigraphs.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Provably powerful graph neural networks for directed multigraphs

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation b68fd062-9e7a-4b14-8488-b3651516815d · outbound

This paper cites Demystifying fraudulent transactions and illicit nodes in the bitcoin network for financial forensics.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Demystifying fraudulent transactions and illicit nodes in the bitcoin network for financial forensics

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 020178cc-3cc2-4b6d-9c2e-2bdfa4176160 · outbound

This paper cites Scalable motif counting for large-scale temporal graphs.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Scalable motif counting for large-scale temporal graphs

Reference 13

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raw_fallback, observed 2026-08-05T14:51:12.782014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 1dd120b1-36cd-4ad4-99b1-61b4fae9f617 · outbound

This paper cites Inductive representation learning on large graphs.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Inductive representation learning on large graphs

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:51:12.507078Z digest=sha256:4f4e25e1812cafecc9d9a1da62dd734d94bccd2f862cf2dd76ab4ee78bd5486c

Observation 0cf0921c-16e5-4325-a82e-955b7a9cdfa3 · outbound

This paper cites Samcl: Subgraph-aligned multiview contrastive learning for graph anomaly detection.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Samcl: Subgraph-aligned multiview contrastive learning for graph anomaly detection

Reference 15

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raw_fallback, observed 2026-08-05T14:51:12.770470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation e7424523-50ee-4a81-ba8d-f10fc9981343 · outbound

This paper cites Collaborative fraud detection: How collaboration impacts fraud detection.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Collaborative fraud detection: How collaboration impacts fraud detection

Reference 16

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raw_fallback, observed 2026-08-05T14:51:12.763139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 7f0b8e7d-02c0-4392-adf4-5ee686928b63 · outbound

This paper cites Hybrid-order anomaly detection on attributed networks.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Hybrid-order anomaly detection on attributed networks

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 4ebf8427-afca-482c-8719-7766d7d0f466 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Semi-Supervised Classification with Graph Convolutional Networks

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8e43e4fe-8f90-4504-b980-4945452fc82e · outbound

This paper cites Data mining techniques for the detection of fraudulent financial statements.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Data mining techniques for the detection of fraudulent financial statements

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation fe8b8afc-4a99-474a-9c61-767500e632bb · outbound

This paper cites Rev2: Fraudulent user prediction in rating platforms.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Rev2: Fraudulent user prediction in rating platforms

Reference 20

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raw_fallback, observed 2026-08-05T14:51:12.739422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:51:12.523230Z digest=sha256:971365b4f50b4d86410906a5bb050a9ed7c4cf03e60b66fc0d183539db266ff9

Observation 5146fd98-5eda-4aeb-a441-cfaa91dfc1c2 · outbound

This paper cites Edge weight prediction in weighted signed networks.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Edge weight prediction in weighted signed networks

Reference 21

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raw_fallback, observed 2026-08-05T14:51:12.730458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation c437033b-1686-4f52-a0b0-dab331e4ca6c · outbound

This paper cites Slade: Detecting dynamic anomalies in edge streams without labels via self-supervised learning.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Slade: Detecting dynamic anomalies in edge streams without labels via self-supervised learning

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 68f0f942-4e9e-436b-97bb-4680224b372e · outbound

This paper cites Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation c7f133b2-4bf0-400d-9e21-082d71754125 · outbound

This paper cites Using motif transitions for temporal graph generation.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Using motif transitions for temporal graph generation

Reference 24

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raw_fallback, observed 2026-08-05T14:51:12.715356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 67803bfe-f1a4-4314-96f6-291b7224cb58 · outbound

This paper cites Motifs in temporal networks.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Motifs in temporal networks

Reference 25

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raw_fallback, observed 2026-08-05T14:51:12.708206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 30f21066-cde3-47cb-b527-e0ed2dab8bcd · outbound

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

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Pytorch: An imperative style, high-performance deep learning library

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 08443b1f-6537-4dc7-b1ab-26ffc7814b10 · outbound

This paper cites Raphtory.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Raphtory

Reference 27

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raw_fallback, observed 2026-08-05T14:51:12.697576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation e362c06a-4b55-4b3f-bb9a-287ebb3a05e9 · outbound

This paper cites Scalable temporal motif densest subnetwork discovery.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Scalable temporal motif densest subnetwork discovery

Reference 28

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raw_fallback, observed 2026-08-05T14:51:12.690232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 273d4fbb-9c9e-4027-badf-0d230b9d8766 · outbound

This paper cites Attention is all you need.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Attention is all you need

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:51:12.550450Z digest=sha256:eae97e919053535372d911ce30127824d3b64a7a7a0d74b80992b99d1bc0ec4a

Observation ae814371-f92b-4927-acb2-7cf009b1122a · outbound

This paper cites Graph Attention Networks.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Graph Attention Networks

Reference 30

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no resolver link, observed 2026-08-05T14:51:12.553279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:51:12.553279Z digest=sha256:a2a5f08149317093cae9be795d0476186f44c692eef0156f82e98b97c240eabd

Observation c94d2b45-b4d5-4cca-9898-dd5dd1d06ca1 · outbound

This paper cites Financial default prediction via motif-preserving graph neural network with curriculum learning.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Financial default prediction via motif-preserving graph neural network with curriculum learning

Reference 31

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raw_fallback, observed 2026-08-05T14:51:12.679216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:51:12.556157Z digest=sha256:d426377f12048d43a4fb3c590b9d92f309d12b527562376c739e19225fe0834e

Observation 208d3c2c-9811-4c86-bae0-1dd95cf7c8a4 · outbound

This paper cites Mcogcn-motif high-order feature-guided embedding learning framework for social link prediction.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Mcogcn-motif high-order feature-guided embedding learning framework for social link prediction

Reference 32

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raw_fallback, observed 2026-08-05T14:51:12.671138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:51:12.558832Z digest=sha256:c1839ee20b63b70b7eabd33edf445080512e590045fc69291c77f7624a19792d

Observation 7da16d9c-85a2-4f7c-b127-12f5943795d0 · outbound

This paper cites Motif-consistent counterfactuals with adversarial refinement for graph-level anomaly detection.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Motif-consistent counterfactuals with adversarial refinement for graph-level anomaly detection

Reference 33

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raw_fallback, observed 2026-08-05T14:51:12.662447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:51:12.561520Z digest=sha256:51fba0c1f74664628ef1549d03c66ff2abb14e6aa34ba23da491ef68d6c2f929

Observation aa6af963-4653-4a77-aca9-fb3c820c632f · outbound

This paper cites Temporal-amount snapshot multigraph for ethereum transaction tracking.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Temporal-amount snapshot multigraph for ethereum transaction tracking

Reference 34

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raw_fallback, observed 2026-08-05T14:51:12.653025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:51:12.564267Z digest=sha256:9f40ed555ba39cc01c4e74730238d7a8d77f1451378865f7440e87af654e248d

Observation 3b8f6189-a787-4281-8771-b86f7d08dd80 · outbound

This paper cites Motif-level anomaly detection in dynamic graphs.

ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks Motif-level anomaly detection in dynamic graphs

Reference 35

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raw_fallback, observed 2026-08-05T14:51:12.643893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:51:12.566984Z digest=sha256:a4d5d840f27f1c966843707c1423e90222f65f931deca7f1384a00ec8967beee

Pith citing papers

No inbound Pith citation observations are available.