Paper Citation Record · LEDGER

Harnessing Large Language Models for Disaster Management: A Survey

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:2501.06932.

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

pith.paper-citation-record.v1
2501.06932 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:52:41.307275Z

measured 33 of 33 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T12:45:16.298896Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

31 of 31 outbound references displayed

verified exact4
verified fuzzy15
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch6

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 754bafce-da0f-4904-a0f3-0f97677e28d1 · outbound

This paper cites Natural Hazards, 111(1):851–875.

Harnessing Large Language Models for Disaster Management: A Survey Natural Hazards, 111(1):851–875

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.807793Z

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.

Observation f8d08959-b0fa-4bfa-b9af-f46162b9cd4b · outbound

This paper cites In 2024 International Conference on Emerging Sys- tems and Intelligent Computing (ESIC), pages 151–.

Harnessing Large Language Models for Disaster Management: A Survey In 2024 International Conference on Emerging Sys- tems and Intelligent Computing (ESIC), pages 151–

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.782875Z

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.

Observation f7e71ce8-2dbf-4636-9130-57be9f371416 · outbound

This paper cites In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) , pages 1614–1619.

Harnessing Large Language Models for Disaster Management: A Survey In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) , pages 1614–1619

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.754694Z

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.

Observation 6dd2a581-27bc-4f65-9907-03fd83a1362e · outbound

This paper cites Stochastic Environ- mental Research and Risk Assessment , 36(2):473– 493.

Harnessing Large Language Models for Disaster Management: A Survey Stochastic Environ- mental Research and Risk Assessment , 36(2):473– 493

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.741966Z

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.

Observation 38e96ae2-1e04-43d6-bd10-b02603c0fb72 · outbound

This paper cites FloodLense: A Framework for ChatGPT-based Real-time Flood Detection.

Harnessing Large Language Models for Disaster Management: A Survey FloodLense: A Framework for ChatGPT-based Real-time Flood Detection

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:52:41.543865Z

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.

Observation 63e9b535-8917-4b02-a2a3-b6699b0d686a · outbound

This paper cites Zero-Shot Classification of Crisis Tweets Using Instruction-Finetuned Large Language Models.

Harnessing Large Language Models for Disaster Management: A Survey Zero-Shot Classification of Crisis Tweets Using Instruction-Finetuned Large Language Models

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:52:41.486410Z

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.

Observation 8beda049-e8fc-4d51-8a14-a1a4ab644c63 · outbound

This paper cites A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media.

Harnessing Large Language Models for Disaster Management: A Survey A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:52:41.464247Z

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.

Observation 3de1a271-d1b9-4bcb-b491-8baf087a3bd3 · outbound

This paper cites Relevance Classification of Flood-related Twitter Posts via Multiple Transformers.

Harnessing Large Language Models for Disaster Management: A Survey Relevance Classification of Flood-related Twitter Posts via Multiple Transformers

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:52:41.446850Z

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.

Observation 4c839fef-ca2e-4aad-84f0-c4d4176313a8 · outbound

This paper cites EAI Endorsed Transactions on Scalable Information Systems, 8(31):e8–e8.

Harnessing Large Language Models for Disaster Management: A Survey EAI Endorsed Transactions on Scalable Information Systems, 8(31):e8–e8

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.714604Z

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.

Observation eefd3d22-d9ee-4c7d-bdf8-fd910a4af969 · outbound

This paper cites In 2024 IEEE Con- ference on Artificial Intelligence (CAI), pages 851–.

Harnessing Large Language Models for Disaster Management: A Survey In 2024 IEEE Con- ference on Artificial Intelligence (CAI), pages 851–

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.684204Z

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.

Observation 84123b8d-eae9-4581-b1a7-3914900c6658 · outbound

This paper cites NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters.

Harnessing Large Language Models for Disaster Management: A Survey NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:52:41.410632Z

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.

Observation 28612800-2382-4413-bcb8-e89aa9176e27 · outbound

This paper cites In Proceedings of the Joint Workshop on Linguistic An- notation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), pages 133–143.

Harnessing Large Language Models for Disaster Management: A Survey In Proceedings of the Joint Workshop on Linguistic An- notation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), pages 133–143

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.669289Z

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.

Observation f5dae4ad-90af-4357-a489-58de2da68d63 · outbound

This paper cites Transformer-based Multi-task Learning for Disaster Tweet Categorisation.

Harnessing Large Language Models for Disaster Management: A Survey Transformer-based Multi-task Learning for Disaster Tweet Categorisation

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:52:41.377014Z

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.

Observation 2c3d06fb-c7fc-4481-afab-99e60ec2cc5e · outbound

This paper cites In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, pages 1–7.

Harnessing Large Language Models for Disaster Management: A Survey In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, pages 1–7

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.656755Z

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.

Observation 02ee1d14-00cd-4965-8a6a-65a6fdca37c5 · outbound

This paper cites WildfireGPT: Tailored Large Language Model for Wildfire Analysis.

Harnessing Large Language Models for Disaster Management: A Survey WildfireGPT: Tailored Large Language Model for Wildfire Analysis

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T20:52:41.292027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6630549-80b5-4b47-aab9-1ba3bdeae121 · outbound

This paper cites In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 1713–1726.

Harnessing Large Language Models for Disaster Management: A Survey In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 1713–1726

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.643344Z

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.

Observation b287956e-65f6-4e00-8241-2a20d5e251c6 · outbound

This paper cites CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics.

Harnessing Large Language Models for Disaster Management: A Survey CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T20:52:41.302481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ceeebb9-2eda-4a2b-b355-3fea6a216068 · outbound

This paper cites #earthquake.

Harnessing Large Language Models for Disaster Management: A Survey #earthquake

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.630262Z

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.

Observation d9ddb0ab-23bf-4a4e-ab63-0796a74e2211 · outbound

This paper cites Youngsun Jang, Maryam Moshrefizadeh, Abir Moham- mad Hadi, Kwanghee Won, and John Kim.

Harnessing Large Language Models for Disaster Management: A Survey Youngsun Jang, Maryam Moshrefizadeh, Abir Moham- mad Hadi, Kwanghee Won, and John Kim

Reference 156

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.768314Z

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.

Observation ce5a683e-0930-48ad-bf74-944cc012df31 · outbound

This paper cites Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance.

Harnessing Large Language Models for Disaster Management: A Survey Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance

Reference 859

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:52:41.429176Z

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.

Observation 1950046d-7d46-4042-9924-37be14eb5d38 · outbound

This paper cites Unsupervised Cross-lingual Representation Learning at Scale.

Harnessing Large Language Models for Disaster Management: A Survey Unsupervised Cross-lingual Representation Learning at Scale

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-10T20:52:41.176666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31319350-6889-4c5b-9192-de2b1ab0dbd8 · outbound

This paper cites In Proceedings of the 18th ACM conference on com- puter supported cooperative work & social comput- ing, pages 994–1009.

Harnessing Large Language Models for Disaster Management: A Survey In Proceedings of the 18th ACM conference on com- puter supported cooperative work & social comput- ing, pages 994–1009

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.699658Z

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.

Observation f311c16d-c8a6-4904-81e3-3a6d9777a155 · outbound

This paper cites In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 1638–1643, Portorož, Slovenia.

Harnessing Large Language Models for Disaster Management: A Survey In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 1638–1643, Portorož, Slovenia

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.795698Z

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.

Observation fb12676d-09a4-4943-913b-a88ec8d3df85 · outbound

This paper cites QuakeBERT: Accurate Classification of Social Media Texts for Rapid Earthquake Impact Assessment.

Harnessing Large Language Models for Disaster Management: A Survey QuakeBERT: Accurate Classification of Social Media Texts for Rapid Earthquake Impact Assessment

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:52:41.563444Z

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.

Observation b68e245e-4aad-4a24-b2a4-1162d9fb93fd · outbound

This paper cites Emma McDaniel, Samuel Scheele, and Jeff Liu.

Harnessing Large Language Models for Disaster Management: A Survey Emma McDaniel, Samuel Scheele, and Jeff Liu

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.728416Z

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.

Observation ab912ef1-f131-4a1b-9d81-593c799b83d2 · outbound

This paper cites Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training.

Harnessing Large Language Models for Disaster Management: A Survey Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T20:52:41.277487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ead7de34-5730-427a-a9bc-a52c665af751 · outbound

This paper cites AI-assisted Protective Action: Study of ChatGPT as an Information Source for a Population Facing Climate Hazards.

Harnessing Large Language Models for Disaster Management: A Survey AI-assisted Protective Action: Study of ChatGPT as an Information Source for a Population Facing Climate Hazards

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:52:41.512623Z

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.

Observation f041a5b9-b30d-4222-86cf-0c1dda21c9dd · outbound

This paper cites did you feel it?.

Harnessing Large Language Models for Disaster Management: A Survey did you feel it?

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:41.821062Z

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.

Observation f3d686bd-11d4-4d54-9b11-41a5c091b127 · outbound

This paper cites Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery.

Harnessing Large Language Models for Disaster Management: A Survey Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery

Reference 2023

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:52:41.615243Z

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.

Observation 77b37a7e-7902-4538-9fb8-169b26e00e05 · outbound

This paper cites Language Models are Few-Shot Learners.

Harnessing Large Language Models for Disaster Management: A Survey Language Models are Few-Shot Learners

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T20:52:41.170506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4da0bf0-ba79-4fa5-9913-53d98c8821d2 · outbound

This paper cites BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency.

Harnessing Large Language Models for Disaster Management: A Survey BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-10T20:52:41.226230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0f164dff-0bad-439c-9255-aa3e1a446d28 · inbound

A Survey of Scaling in Large Language Model Reasoning cites this paper.

A Survey of Scaling in Large Language Model Reasoning Harnessing Large Language Models for Disaster Management: A Survey

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:22:08.764590Z

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.

Observation b3b2ddf3-b968-44fb-9087-774a5dc3c475 · inbound

RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagery cites this paper.

RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagery Harnessing Large Language Models for Disaster Management: A Survey

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-26T12:49:28.560144Z

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.