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Paper Citation Record · LEDGER

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps

As of 20 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2505.18426.

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

pith.paper-citation-record.v1
2505.18426 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:29.505294Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

53 of 53 outbound references displayed

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  • verified fuzzy24
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External citation measurements

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Outbound references

Observation 1b1051fc-882d-47ce-b81f-e918b3200596 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 1

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Observation 720dafe1-d955-4430-a7b7-aeaf95e447b8 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 2

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This paper cites We explore how generative models have evolved, the nature and taxonomy of hallucinations, and the approaches taken to detect and mitigate them.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps We explore how generative models have evolved, the nature and taxonomy of hallucinations, and the approaches taken to detect and mitigate them

Reference 3

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Observation eed21021-cace-4ded-9aa3-61ad738438ed · outbound

This paper cites To mitigate these complexities, context -augmented LLMs offer a practical solution.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps To mitigate these complexities, context -augmented LLMs offer a practical solution

Reference 4

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This paper cites Alabama" and the state of.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Alabama" and the state of

Reference 5

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Observation 93250a35-0e73-489e-99cf-9533289c70fc · outbound

This paper cites statutes and legislation.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps statutes and legislation

Reference 6

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Observation f5e9da3a-75ea-4234-b52e-ddc15dcb23e2 · outbound

This paper cites Figure 9 showcases a sample output 4 produced by our RAG-powered GPT architecture.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Figure 9 showcases a sample output 4 produced by our RAG-powered GPT architecture

Reference 7

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Observation cfcc8a9e-d347-4a82-8630-b81a27b03706 · outbound

This paper cites This limitation is 29 significant, as inaccurate outputs can misidentify legislative gaps and complicate the work of legal 30 and policy stakeholders.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps This limitation is 29 significant, as inaccurate outputs can misidentify legislative gaps and complicate the work of legal 30 and policy stakeholders

Reference 9

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This paper cites Definitions.pdf (14) Identification document.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Definitions.pdf (14) Identification document

Reference 12

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Observation f38ff92f-b2d6-44b4-841e-f04f9d55834b · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 13

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This paper cites § 13A-8-111:.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps § 13A-8-111:

Reference 15

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This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 16

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Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Definitions.pdf

Reference 17

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Observation 55155efc-b1bc-4878-8cdd-1ec0ba3a362b · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 18

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This paper cites identification document.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps identification document

Reference 19

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This paper cites The comparison focuses on the 4 factual accuracy of the responses produced by each model.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps The comparison focuses on the 4 factual accuracy of the responses produced by each model

Reference 20

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Observation 3fbfe7e0-23aa-40f5-9967-9bd1f0731950 · outbound

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Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 21

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This paper cites These methods can scrutinize both questions and outputs for adherence to 25 standards.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps These methods can scrutinize both questions and outputs for adherence to 25 standards

Reference 22

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This paper cites Department of Transportation National University 29 Transportation Center) headquartered at Clemson University, Clemson, South Carolina, USA.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Department of Transportation National University 29 Transportation Center) headquartered at Clemson University, Clemson, South Carolina, USA

Reference 23

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This paper cites All authors reviewed the results 42 and approved the final version of the manuscript.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps All authors reviewed the results 42 and approved the final version of the manuscript

Reference 24

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Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 25

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Observation 7be8a8a0-652e-4002-a054-934a01210ce9 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 26

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Observation 1f7560e2-7dc4-4286-8ba8-4257b8775ac2 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 27

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Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Moss, eds

Reference 28

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Observation 03e7a4e3-6bb5-4f5a-bba9-1edc61a06b58 · outbound

This paper cites Transforming Legal Aid with AI: Training LLMs to Ask Better Questions for Legal Intake.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Transforming Legal Aid with AI: Training LLMs to Ask Better Questions for Legal Intake

Reference 29

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This paper cites How LLM’s Are A Game Changer In Legal Research.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps How LLM’s Are A Game Changer In Legal Research

Reference 30

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Observation 5ebed13f-61c6-40e6-8908-9922de840af9 · outbound

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Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 31

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Observation 1d715de9-950a-4186-844a-60045ea07d5a · outbound

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Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 32

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Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 33

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Observation 5c13b6eb-8f60-420f-82be-a07ef2261ddf · outbound

This paper cites On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark

Reference 34

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This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 35

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Observation 34e7cdf0-16c0-40fb-9de9-2490adc6ea39 · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 36

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This paper cites On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?

Reference 37

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Observation ecf5a247-8d8f-46e1-97c4-0ed839637723 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 38

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Observation 6fd7cd10-f991-425b-8afd-3be8972611d1 · outbound

This paper cites A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:34:30.208104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:27.941451Z digest=sha256:aa306fc72ca24b30de6aa46de3efd9a4c04a764fd06f3162faaedac29d62f4fe

Observation d5011949-8a95-4f9a-ac8f-0c68016928a9 · outbound

This paper cites Enabling Large Language Models to Generate Text with Citations.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Enabling Large Language Models to Generate Text with Citations

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T14:34:28.026754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:34:28.026754Z digest=sha256:dcc49e85f37999cc73adf15be0d445c6e171fd0cb48472b9675a23d806ffe9f8

Observation 3d59132a-5816-4d84-a0c4-b17de8d96541 · outbound

This paper cites Hallucinating Law: Legal Mistakes with Large Language Models Are Pervasive.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Hallucinating Law: Legal Mistakes with Large Language Models Are Pervasive

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:34:31.927118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:28.110914Z digest=sha256:63f0da4533c2be32c287e3f6cee247f6f58fe330b7fa428fd07d031334a217fa

Observation f1fd2429-3168-49c3-a4a3-2e81143b3442 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:34:31.917995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:28.166621Z digest=sha256:e68f2fc6403aaae4ebbb26499ffbb1815768f8c0c417b0c3f7d8d330cb11e074

Observation 481cc88c-e95a-4268-9385-bea4f8b13c88 · outbound

This paper cites RAFT: Adapting Language Model to Domain Specific RAG.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps RAFT: Adapting Language Model to Domain Specific RAG

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T14:34:28.297477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:34:28.297477Z digest=sha256:cc738e35791140651a300278e4bef752ed151d1d9878c5d4293636980346a5de

Observation a6d204cf-97f1-4388-8bd8-0c30c2d66ece · outbound

This paper cites LlamaIndex.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps LlamaIndex

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:34:31.910539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:28.442374Z digest=sha256:67c739d74f19252047560a660d134309f56ef03c413e89db6de48d061d43564b

Observation 5cb95806-dc2c-476d-b1e5-b2ed39ecc3af · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T14:34:28.548212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:34:28.548212Z digest=sha256:924e49b9cd4c018b17eb0894db2f00d4e7a5f9b9fe7168907615a0b2ae417664

Observation e968a917-7edf-4234-85bc-65d991550fa9 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:34:31.894934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:28.688015Z digest=sha256:25d9e4d3b4adfcd2a18d21588352678073e1763a4a2b75086d6cb5613864a841

Observation 63cc7ccc-82a1-437a-95d9-85d787fc5873 · outbound

This paper cites (2024, July 26).

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps (2024, July 26)

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:34:31.887298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:28.772361Z digest=sha256:1768d1ce30478522a7ed23d532807398b65da73e3e729fee37f67655d479a7c6

Observation 760bdeb2-422a-478c-9967-58cd58241109 · outbound

This paper cites (2024, May 7).

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps (2024, May 7)

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:34:31.879559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:28.823365Z digest=sha256:a47ea43336fbffb5dc5ab028625bc0e168021813c875f07f63d773f02677a57e

Observation 94213522-537f-4456-9cc9-570a06a80ad2 · outbound

This paper cites (2024, July 23).

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps (2024, July 23)

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:34:31.794525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:28.861420Z digest=sha256:037cc69c4125202c245dda75c8096b59627968f981873a855af5446681c8a7f6

Observation 2688b8d7-16ae-41be-b92b-c0761967c650 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T14:34:28.930150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:34:28.930150Z digest=sha256:32025e5bd2c9c1bbd71d3abf178925f214bde893748dc250c859549f1283fecd

Observation 1a3b1808-f045-47fc-b1e6-38b9a80049fa · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 52

Resolution
malformed identifier
no resolver link, observed 2026-08-07T14:34:28.963597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:34:28.963597Z digest=sha256:28ce31938934188ad07aa9420693edab5f86a03982bb66c30f69f1ba81dbbc0a

Observation 223cae72-b661-4e2d-af28-6081ddfee0ee · outbound

This paper cites Q., & Artzi, Y.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Q., & Artzi, Y

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:34:31.602422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:29.074701Z digest=sha256:bc64db614a07e5301cf3324ea224adb29a61280ad6c6fa855cf8509476343cd8

Observation 21f933a7-1293-47ca-bf23-21ff3c59ae64 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:34:31.403689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:29.178789Z digest=sha256:c912d4657b140ab4ebd0ec0fd3a7f783261c94b8c4b1c14d64935a920b119138

Observation 6795d1c0-9006-47de-ab43-d5298afd89d9 · outbound

This paper cites OpenAI Platform.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps OpenAI Platform

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:34:31.247689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:29.318885Z digest=sha256:74b9c23c7adb12cbdb1c97a306aaf866401f6c4c7e78a40e08d102e326fffc7b

Observation d1e78430-3c95-43c5-81aa-595c8462e4e8 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:34:31.016954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:29.426083Z digest=sha256:b59a8eccac7f9cdef43e8a7889f969ffbdaf11204951d8d27ba134961f23f317

Observation 6316082b-2964-493a-bbf9-a44608f7f290 · outbound

This paper cites an unresolved cited work.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps Unresolved cited work

Reference 57

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:34:30.736922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:29.505294Z digest=sha256:bd46b08fdb350bdc976992b513283809cc486cd9f000537b60f5025182099681

Observation 75f71623-bdc1-4135-9a98-41a54b12aa0a · outbound

This paper cites What 37 are the maximum penalties for failing to follow the data breach notification statutes in Ohio and 38 Oklahoma?.

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps What 37 are the maximum penalties for failing to follow the data breach notification statutes in Ohio and 38 Oklahoma?

Reference 2023

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T14:34:32.023347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:34:25.833637Z digest=sha256:97f9a298f502f387063133579e4c7ffe7fc43839f7b1fd255924fa280425d925

Pith citing papers

No inbound Pith citation observations are available.