Paper Citation Record · LEDGER

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 2 inbound Pith citation observations for arXiv:2506.13796.

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

pith.paper-citation-record.v1
2506.13796 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:29:09.858550Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T23:07:21.558834Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:35:46.040017Z

Reference resolution

30 of 30 outbound references displayed

verified exact8
verified fuzzy5
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6a4fa6d6-35e9-451e-9f45-b625fcb755de · outbound

This paper cites Climate change as a global amplifier of human--wildlife conflict.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Climate change as a global amplifier of human--wildlife conflict

Reference 1

Resolution: verified exact; doi, observed 2026-08-07T04:29:11.183112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e707386b-0485-4734-b643-0e21094453d9 · outbound

This paper cites Cheap talk in corporate climate commitments: The role of active institutional ownership, signaling, materiality, and sentiment.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Cheap talk in corporate climate commitments: The role of active institutional ownership, signaling, materiality, and sentiment

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:12.377582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cd8d0f66-4bfc-4c86-827e-e565b45a6bc5 · outbound

This paper cites Detecting deception using natural language processing and machine learning in datasets on covid-19 and climate change.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Detecting deception using natural language processing and machine learning in datasets on covid-19 and climate change

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:07.047219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a303039f-eeae-4918-93b8-2d7c579a656b · outbound

This paper cites Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies

Reference 4

Resolution: verified exact; doi, observed 2026-08-07T04:29:11.024010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b1ac5d8f-dddf-457e-bdc2-f230c7294d49 · outbound

This paper cites Preparedllm: Effective pre-pretraining framework for domain-specific large language models.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Preparedllm: Effective pre-pretraining framework for domain-specific large language models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:07.271583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80acc638-304f-46f8-b631-85aeeb6f1d7f · outbound

This paper cites Jiuzhou: Open foundation language models and effective pre-training framework for geoscience.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Jiuzhou: Open foundation language models and effective pre-training framework for geoscience

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:07.374950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a48e8dd-f951-499e-9240-e26fb165d222 · outbound

This paper cites Cody, Andrew J.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Cody, Andrew J

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:12.250724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c2999586-4887-44ce-b2e2-fe60050fc3f7 · outbound

This paper cites Free dolly: Introducing the world's first truly open instruction-tuned llm, 2023.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Free dolly: Introducing the world's first truly open instruction-tuned llm, 2023

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:07.574615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c2fef1e-ccd6-478e-85c5-e945b692ab4f · outbound

This paper cites K2: A foundation language model for geoscience knowledge understanding and utilization.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries K2: A foundation language model for geoscience knowledge understanding and utilization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:07.673569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a92112e0-d7fb-48d9-be24-781e3dd1afdc · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:07.766852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f23add95-5d40-4dde-b29e-bcf673da8cce · outbound

This paper cites Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:07.897574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a13205c-771c-4a2f-b323-30fc2ac97932 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries LoRA: Low-Rank Adaptation of Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:08.026112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24962ed8-28b7-48a1-b6fc-a7a9e336519a · outbound

This paper cites Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:08.081429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc73fa35-3b83-49fe-b103-86f4fd33298a · outbound

This paper cites #InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries #InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:08.196359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c8d12f5-854e-481d-bcd6-a783b1f84878 · outbound

This paper cites Understanding the impact of climate change on critical infrastructure through nlp analysis of scientific literature.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Understanding the impact of climate change on critical infrastructure through nlp analysis of scientific literature

Reference 15

Resolution: verified exact; raw_fallback, observed 2026-08-07T04:29:11.362424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9709ecea-ea11-4bef-8b37-32a3400306cd · outbound

This paper cites Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:29:10.766062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e129df49-c590-4a75-b1cb-9e04117a1f6c · outbound

This paper cites A rabic mini- C limate GPT : A climate change and sustainability tailored A rabic LLM.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries A rabic mini- C limate GPT : A climate change and sustainability tailored A rabic LLM

Reference 17

Resolution: verified exact; doi, observed 2026-08-07T04:29:10.564582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 49a58e92-56cf-4fb9-97bf-933102613a39 · outbound

This paper cites Instruction Tuning with GPT-4.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Instruction Tuning with GPT-4

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:08.613436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b0d0e2b-d5b6-434c-8e5d-a8cf7b7fd976 · outbound

This paper cites Climate bot: A machine reading comprehension system for climate change question answering.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Climate bot: A machine reading comprehension system for climate change question answering

Reference 19

Resolution: verified exact; doi, observed 2026-08-07T04:29:10.360964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 93db84ff-1604-4c2f-a77f-2b38f4c79183 · outbound

This paper cites Harnessing the potential of nature-based solutions for mitigating and adapting to climate change.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Harnessing the potential of nature-based solutions for mitigating and adapting to climate change

Reference 20

Resolution: verified exact; doi, observed 2026-08-07T04:29:10.148592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e5f74533-ca62-49d4-b01d-980fd864b634 · outbound

This paper cites Analyzing the dynamics of climate change discourse on twitter: A new annotated corpus and multi-aspect classification.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Analyzing the dynamics of climate change discourse on twitter: A new annotated corpus and multi-aspect classification

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:12.146332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40e96631-88e0-4971-85b1-3c1432ff0a05 · outbound

This paper cites Environmental claim detection.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Environmental claim detection

Reference 22

Resolution: verified exact; doi, observed 2026-08-07T04:29:10.047978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 82c9956c-b4f6-4b5d-89ae-683586cf9701 · outbound

This paper cites ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:09.130624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e6f567e-1703-4644-a431-cfa0adf2c645 · outbound

This paper cites Deep climate change: A dataset and adaptive domain pre-trained language models for climate change related tasks.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Deep climate change: A dataset and adaptive domain pre-trained language models for climate change related tasks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:11.967299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 567f3d28-c9d7-4424-b5e8-769f71dfc14b · outbound

This paper cites Chatclimate: Grounding conversational ai in climate science.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Chatclimate: Grounding conversational ai in climate science

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:09.354392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d29fa0a3-97fa-4338-b629-7435610b1d4f · outbound

This paper cites ClimateBert: A Pretrained Language Model for Climate-Related Text.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries ClimateBert: A Pretrained Language Model for Climate-Related Text

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:09.459513Z

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Observation dee53c6c-156f-4037-b178-53390570d484 · outbound

This paper cites write newline.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries write newline

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:09.588043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c807925-fc03-4eac-8934-567dc63f2d26 · outbound

This paper cites @esa (Ref.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries @esa (Ref

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:09.692501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f8296e4-f1d1-42ca-b0e5-8148b71ddc6c · outbound

This paper cites an unresolved cited work.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Unresolved cited work

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:09.767308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 313873d8-8d05-4efb-aa9f-323009fdfbf6 · outbound

This paper cites Tackling Climate Change with Machine Learning.

ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries Tackling Climate Change with Machine Learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:11.769134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 40b917c3-6ea7-4084-8ac8-d6e586d9966e · inbound

Earth Science Foundation Models: From Perception to Reasoning and Discovery cites this paper.

Earth Science Foundation Models: From Perception to Reasoning and Discovery ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:08:03.624333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3b5f2aea-caf7-486a-af9e-38cca43f2a5e · inbound

Earth Science Foundation Models: From Perception to Reasoning and Discovery cites this paper.

Earth Science Foundation Models: From Perception to Reasoning and Discovery ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:35:46.041840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.