Paper Citation Record · LEDGER

Food for thought: How can machine learning help better predict and understand changes in food prices?

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2412.06472.

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

pith.paper-citation-record.v1
2412.06472 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:41:06.512116Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

20 of 20 outbound references displayed

verified exact0
verified fuzzy10
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 873d7af8-9adc-4007-9bd1-2e69d6b52ec7 · outbound

This paper cites Chronos: Learning the Language of Time Series.

Food for thought: How can machine learning help better predict and understand changes in food prices? Chronos: Learning the Language of Time Series

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.400912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eb3a403-b920-43ba-8bc8-bd2e7833df4e · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Food for thought: How can machine learning help better predict and understand changes in food prices? On the Opportunities and Risks of Foundation Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.406865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd4742ee-0955-4b80-98d9-e5b1159b5f24 · outbound

This paper cites Did grain futures prices overreact to the russia--ukraine war due to herding? Journal of Commodity Markets, 35: 0 100422, 2024.

Food for thought: How can machine learning help better predict and understand changes in food prices? Did grain futures prices overreact to the russia--ukraine war due to herding? Journal of Commodity Markets, 35: 0 100422, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:07.000115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3a27c135-5983-49f4-b38a-fa3f1111a3a3 · outbound

This paper cites Digital traceability in agri-food supply chains: A comparative analysis of oecd member countries.

Food for thought: How can machine learning help better predict and understand changes in food prices? Digital traceability in agri-food supply chains: A comparative analysis of oecd member countries

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:06.980145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b6f9e11f-309c-486b-a33d-6d9bc3caa2f9 · outbound

This paper cites Implications of carbon pricing on food affordability and agri-food sector in canada: A scoping review.

Food for thought: How can machine learning help better predict and understand changes in food prices? Implications of carbon pricing on food affordability and agri-food sector in canada: A scoping review

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:06.959221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 434a032c-fd24-4e93-8eb7-a66fafe0780a · outbound

This paper cites Timegpt-1, 2023.

Food for thought: How can machine learning help better predict and understand changes in food prices? Timegpt-1, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.429760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed7ee458-9291-4d74-9096-2454f94c6704 · outbound

This paper cites Large language models are zero-shot time series forecasters.

Food for thought: How can machine learning help better predict and understand changes in food prices? Large language models are zero-shot time series forecasters

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.435890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8967260-9f2d-4a94-a42e-82422cb52a74 · outbound

This paper cites Forecasting: principles and practice.

Food for thought: How can machine learning help better predict and understand changes in food prices? Forecasting: principles and practice

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:06.913760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 21b553ba-baf4-43bd-add5-2f47554bfed4 · outbound

This paper cites Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook.

Food for thought: How can machine learning help better predict and understand changes in food prices? Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.446682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93a6acfd-4a77-4a25-a0aa-387d7a95e262 · outbound

This paper cites Food Price Volatility and Its Implications for Food Security and Policy.

Food for thought: How can machine learning help better predict and understand changes in food prices? Food Price Volatility and Its Implications for Food Security and Policy

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:06.896053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation edc350cb-6388-441c-87a4-98b2b96b0679 · outbound

This paper cites Kupferschmidt, Cody Kupferschmidt , Joshua A.

Food for thought: How can machine learning help better predict and understand changes in food prices? Kupferschmidt, Cody Kupferschmidt , Joshua A

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:06.875175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1ee706d7-cf3e-4763-9b2c-fce70be990ad · outbound

This paper cites The 2021--22 surge in inflation.

Food for thought: How can machine learning help better predict and understand changes in food prices? The 2021--22 surge in inflation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:06.853904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 360b8654-4edc-4ce1-aef4-cb75dfb6024d · outbound

This paper cites Kupferschmidt, J.A.

Food for thought: How can machine learning help better predict and understand changes in food prices? Kupferschmidt, J.A

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.470190Z

Source-reported events for the cited work

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Observation a7a91d24-c793-41ee-bee0-af3e4a3c5913 · outbound

This paper cites Enhancing food price forecasts in canada: An integration of expert-driven covariates and advanced ML approaches.

Food for thought: How can machine learning help better predict and understand changes in food prices? Enhancing food price forecasts in canada: An integration of expert-driven covariates and advanced ML approaches

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:06.833048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fa23c1dc-f7d6-4e9d-b23f-3c6a0872bca5 · outbound

This paper cites Temporal fusion transformers for interpretable multi-horizon time series forecasting.

Food for thought: How can machine learning help better predict and understand changes in food prices? Temporal fusion transformers for interpretable multi-horizon time series forecasting

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.480008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0589ef7-22cb-49c2-a863-edc4841d6017 · outbound

This paper cites The m4 competition: Results, findings, conclusion and way forward.

Food for thought: How can machine learning help better predict and understand changes in food prices? The m4 competition: Results, findings, conclusion and way forward

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.485472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07cbc95b-0225-494a-8ffc-ad5e2c89af10 · outbound

This paper cites LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language.

Food for thought: How can machine learning help better predict and understand changes in food prices? LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.492230Z

Source-reported events for the cited work

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Observation 139b5fde-09bf-4a05-8d32-b63dd13077ef · outbound

This paper cites DeepAR : Probabilistic forecasting with autoregressive recurrent networks.

Food for thought: How can machine learning help better predict and understand changes in food prices? DeepAR : Probabilistic forecasting with autoregressive recurrent networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:06.781234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 41b741dd-b1b2-4f56-8e86-77ec5fd9a0a1 · outbound

This paper cites Generating personas using llms and assessing their viability.

Food for thought: How can machine learning help better predict and understand changes in food prices? Generating personas using llms and assessing their viability

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:41:06.760275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6395259b-411b-4477-82b2-5624125cec34 · outbound

This paper cites Context is Key: A Benchmark for Forecasting with Essential Textual Information.

Food for thought: How can machine learning help better predict and understand changes in food prices? Context is Key: A Benchmark for Forecasting with Essential Textual Information

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:06.512116Z

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Pith citing papers

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