Pith. sign in

Paper Citation Record · LEDGER

Adaptations of AI models for querying the LandMatrix database in natural language

As of 19 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2412.12961.

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

pith.paper-citation-record.v1
2412.12961 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:36:21.262459Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

13 of 13 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation df55498f-726e-4cf7-ac32-7cfa81aa2082 · outbound

This paper cites Boche, T.

Adaptations of AI models for querying the LandMatrix database in natural language Boche, T

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:36:21.561805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T13:36:21.186083Z digest=sha256:acafb7c5c367eb33e9f4f3b7feb309c41fec52088e23567dc55d529c51c4e111

Observation e62fc161-d0af-4e04-8f88-d9187f272f26 · outbound

This paper cites RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture.

Adaptations of AI models for querying the LandMatrix database in natural language RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.191997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.191997Z digest=sha256:97905b507dce18bcd35a210a0c5ec547724afaf9859cae4d1867cec30983db61

Observation df9764f5-6c96-4388-a0f5-33c7b36d7bf6 · outbound

This paper cites Language Models are Few-Shot Learners.

Adaptations of AI models for querying the LandMatrix database in natural language Language Models are Few-Shot Learners

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.197973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.197973Z digest=sha256:1fc12e57913554b8ed3df3baf9d9cb8df25c2431f2dd0ca8e635b398dba098c1

Observation 2bbf4aba-b3f3-44a4-9a0b-1e5e5bac6ced · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Adaptations of AI models for querying the LandMatrix database in natural language BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.203858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.203858Z digest=sha256:c680afceb984f3b3467661211310a1c2f873fae80dc97b536efd90c050f67693

Observation ae2736f6-8443-4884-829b-164d472bd952 · outbound

This paper cites an unresolved cited work.

Adaptations of AI models for querying the LandMatrix database in natural language Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:36:21.543252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T13:36:21.210011Z digest=sha256:83e70423019899c23ab6fc8b3ae7b513f29722e168fcd2e4d8f597438a090ad1

Observation b75ea5a1-0432-4f83-9b79-f47bdf77e5d2 · outbound

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

Adaptations of AI models for querying the LandMatrix database in natural language Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.215850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.215850Z digest=sha256:9f4feae5df6a11dedb6cdc845695cd7147a6db74ce6ece6ca22a326a2cb07005

Observation 5c23e6a1-a04c-4c99-ae77-b2a0bfb564b5 · outbound

This paper cites SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models.

Adaptations of AI models for querying the LandMatrix database in natural language SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.228661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.228661Z digest=sha256:5ab9521912197d70c9c4ca92004682d34ff7f37e5044ae6f290e3c5f2f37ea96

Observation a2962e1a-73fd-4abf-b807-cc5088128ec2 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Adaptations of AI models for querying the LandMatrix database in natural language Code Llama: Open Foundation Models for Code

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.234543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.234543Z digest=sha256:1d1310b986033723729e67eed30183acf93d91d739c3677d73cdf22dd1c3cd5a

Observation b3cc4831-66bb-445b-8aa7-b6c95d50ed9b · outbound

This paper cites an unresolved cited work.

Adaptations of AI models for querying the LandMatrix database in natural language Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:36:21.522137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T13:36:21.240779Z digest=sha256:bb0e006325ee22c75fb5949c3e3828eaaaded5667f6b6f290dbbbda4f16ffa15

Observation 1a27c1cc-6d60-43af-9789-e80d871c041e · outbound

This paper cites Attention Is All You Need.

Adaptations of AI models for querying the LandMatrix database in natural language Attention Is All You Need

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.246010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.246010Z digest=sha256:fbeef3349dfd10e652ab800a2a15118e204136beab73ebed9bf78d3b8284966e

Observation a975bec0-5640-4fbe-9c96-c339c6298b0e · outbound

This paper cites Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond.

Adaptations of AI models for querying the LandMatrix database in natural language Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.251862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.251862Z digest=sha256:bef18b74f14be3bdb4f3af30352a7f3060c228d50208b65644aac49e1a933f25

Observation d28edcee-556d-4d92-ac6b-9b7ec355bc85 · outbound

This paper cites A Survey of Large Language Models.

Adaptations of AI models for querying the LandMatrix database in natural language A Survey of Large Language Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.257658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.257658Z digest=sha256:9c51b4936ef9161cd5d019b65adbfecd55d44ef37b8cbd6b32f5b184fddb5bf6

Observation 89329975-0516-42f9-8d96-f1363ed89489 · outbound

This paper cites LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset.

Adaptations of AI models for querying the LandMatrix database in natural language LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:21.262459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:36:21.262459Z digest=sha256:d5ce6253a8ebe66a9550731ec0f953a1f85499224ef5303ab1a36940e16baf93

Pith citing papers

No inbound Pith citation observations are available.