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Paper Citation Record · LEDGER

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach

As of 10 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2502.07677.

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

pith.paper-citation-record.v1
2502.07677 v3

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:58:26.946663Z

measured 10 of 10 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 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

10 of 10 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 23c5726c-df83-43d7-a7e5-2ae7d89b805a · outbound

This paper cites an unresolved cited work.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-08T11:58:27.361603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T11:58:26.181603Z digest=sha256:bb0ecaa2eed58d6a616343da2c7ad28c05d7a5cffaddc995836f1803d7fa103a

Observation 625102da-a57a-4b9c-bbf3-5378875d2372 · outbound

This paper cites an unresolved cited work.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-08T11:58:27.350620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T11:58:26.552059Z digest=sha256:1298341f3a031eeec1982f27b568c4f58999d2bf8e3ffe4c9830517a42bdd593

Observation 429b2825-ece2-4e6b-b02e-84dcf218294a · outbound

This paper cites an unresolved cited work.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-08T11:58:27.334038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T11:58:26.692539Z digest=sha256:ffc57655c0a06ac91fc26fb7f76cc3360601852d68d86c24ce5ea6b27046f123

Observation 258d6b80-65f3-48bc-8571-362d2fe0d40c · outbound

This paper cites an unresolved cited work.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-08T11:58:27.317960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T11:58:26.777608Z digest=sha256:35a068af4ed3c16c65d8d09c027efa914ba23b92563a9f598fa25c37e5d437e4

Observation c368bc1f-106a-4ed1-9037-8afdccb8df1e · outbound

This paper cites an unresolved cited work.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-08T11:58:27.293492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T11:58:26.879239Z digest=sha256:5e692fd2616279ae174c255991ec48293004a0ac5e4abb1cbf6fc2eeb552808f

Observation 464bc843-2f03-44bd-822c-b564bd27d0a4 · outbound

This paper cites an unresolved cited work.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-08T11:58:27.178894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T11:58:26.926186Z digest=sha256:255ce6f9cc2921b6b258c0f8ca286ae98a32aeabf302db7e5fa533400948868c

Observation 223ff027-8afe-4fc7-9d0e-dfb220a749b3 · outbound

This paper cites an unresolved cited work.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-08T11:58:27.012161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T11:58:26.932938Z digest=sha256:ea5bf30c07a4547f5cbd963b7a3f1c73e862efee1a0e96af70be57093358b74c

Observation f0780a42-c059-4d2f-83f2-8555a4f6f439 · outbound

This paper cites an unresolved cited work.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-08T11:58:27.000195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T11:58:26.937363Z digest=sha256:116189da9fe6561433f5348a7082c2bc496dd9c8c4fd756715df4d11d187b0ad

Observation dd2e243e-ee8c-4ec6-af27-1c518936c248 · outbound

This paper cites Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-08T11:58:26.942064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:58:26.942064Z digest=sha256:56e310647e0520a474f271e77ca06ccb02f7d41a114f579bbd9d4fa6df479614

Observation 82e36069-78f3-48f8-acfb-240ebf682e40 · outbound

This paper cites Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration.

Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-08T11:58:26.946663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:58:26.946663Z digest=sha256:cf467511f6e26da0417a4f9c46e1d31a5db871751b207705ce848e50c3c897e2

Pith citing papers

No inbound Pith citation observations are available.