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Paper Citation Record · LEDGER

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions

As of 12 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2608.01767.

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

pith.paper-citation-record.v1
2608.01767 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:26:01.522592Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

33 of 33 outbound references displayed

  • verified exact2
  • verified fuzzy17
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 524cfa2d-d1ea-48fd-8537-8c0a1ad288fb · outbound

This paper cites https://www.who.int/news-room/fact-sheets/detail/ food-safety.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions https://www.who.int/news-room/fact-sheets/detail/ food-safety

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:02.047261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 5240795b-711c-4ef4-8735-9d791fb7f53a · outbound

This paper cites Tests Show Most Store Honey Isn’t Honey.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Tests Show Most Store Honey Isn’t Honey

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-04T21:26:02.033728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.376377Z digest=sha256:afbbcb6900a7320d8f4d5afa605aad40cac9be6a398bfabd8d08c6569bd71627

Observation e87fa78b-e9ea-482c-b081-6f3478b89569 · outbound

This paper cites https://documents1.worldbank.org/curated/en/958471616983319964/pdf/ China-Food-Safety-ImprovementProject.pdf.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions https://documents1.worldbank.org/curated/en/958471616983319964/pdf/ China-Food-Safety-ImprovementProject.pdf

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-08-04T21:26:01.615924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 2659dabd-d15f-4da3-96a2-86eeac864e7a · outbound

This paper cites & Cassou, E.The safe food imper- ative: Accelerating progress in low-and middle-income countries(World Bank Publications, 2018).

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions & Cassou, E.The safe food imper- ative: Accelerating progress in low-and middle-income countries(World Bank Publications, 2018)

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:02.019559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.385517Z digest=sha256:3522e10c75427056b14c564e0cd5cee9540ebb1ee7e6fe77bc6843d6c054d67c

Observation 98bcc929-56d1-4d4a-a7e8-9d0aa3ba8105 · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 6

Resolution
verified exact
doi, observed 2026-08-04T21:26:01.560770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation c407ddb8-6586-4d2f-8438-7fc5b058ffdf · outbound

This paper cites https://food.ec.europa.eu/ food-safety/rasff en.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions https://food.ec.europa.eu/ food-safety/rasff en

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:02.004072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 2b8a52a5-f615-4136-ad0e-80e370b828b8 · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-04T21:26:01.988653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 5e677a37-097b-4de7-a132-e1ec600b7ddd · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-04T21:26:01.975009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.417905Z digest=sha256:6cd69e066d4ee2b8576a3e31d7e307dadbab6b21bd3b17fb34c8e0a729169447

Observation 8323bd83-87fd-434e-b68f-4fe0ebfded9e · outbound

This paper cites https://https://www.who.int/ publications/i/item/9789240057685.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions https://https://www.who.int/ publications/i/item/9789240057685

Reference 11

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unresolved
no resolver link, observed 2026-08-04T21:26:01.422462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T21:26:01.422462Z digest=sha256:760514e16604d961016cd871b866df6cc0628c7894af365ca79e6a0ce665ae3a

Observation d51ea829-bdcc-46ef-940b-24992d2d56b0 · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-04T21:26:01.958895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation dbe96c56-f682-47ec-8c35-bbd1cab98ad4 · outbound

This paper cites https://www.stats.gov.cn/english/ Statisticaldata/yearbook/.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions https://www.stats.gov.cn/english/ Statisticaldata/yearbook/

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.943148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.430820Z digest=sha256:c759a8172cdfa8df074500ff78fbf3dd487198e746d071b49b2da82a6df10a25

Observation ca970815-7b39-4e8f-8de9-5f44b3eb1eb6 · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-04T21:26:01.928442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.436141Z digest=sha256:5a58001b6acbb0ef2fbdaa8d242622091b663ac01c614a8368b07d5b179b8825

Observation 88bde93d-9a23-4276-8b56-958c81727f09 · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems(2017).

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Attention is all you need.Advances in Neural Information Processing Systems(2017)

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.913106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.440197Z digest=sha256:268b2e182c00d78cd477d13feb576d7ed8cdfae2bedb77661de946b8deb601e1

Observation cd7d4303-4888-4adc-b801-04e3c0a4e32e · outbound

This paper cites & Chen, J.-s.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions & Chen, J.-s

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.897308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 95d96a90-d42e-4d59-aca6-400049d48db1 · outbound

This paper cites https://www.gov.cn/xinwen/2019-02/09/content 5364511.htm.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions https://www.gov.cn/xinwen/2019-02/09/content 5364511.htm

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.881671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.448087Z digest=sha256:eda693ae0234b80143745f14b68d8b9b35789b53ce4de69e160e58ed265b57d5

Observation 19abebf2-b4d5-4881-bedc-a0f48ccc6cb9 · outbound

This paper cites https://www.gov.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions https://www.gov

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.867584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 9edac6a2-1f43-42b8-8e7b-f26bafaccb84 · outbound

This paper cites G.Sampling techniques(john wiley & sons, 1977).

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions G.Sampling techniques(john wiley & sons, 1977)

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.852649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 3531dea3-749c-42cd-bdaa-8a208d058de1 · outbound

This paper cites Binomial confidence intervals and contingency tests: mathematical fun- damentals and the evaluation of alternative methods.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Binomial confidence intervals and contingency tests: mathematical fun- damentals and the evaluation of alternative methods

Reference 20

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unresolved
no resolver link, observed 2026-08-04T21:26:01.460242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T21:26:01.460242Z digest=sha256:87c5fbebb5125a25e0ca6c9a5255d80c7384dc534fae456a46c314b51e5da8be

Observation 56333c75-c264-41aa-b9dc-6c0e312f5b08 · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 21

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unresolved
raw_fallback, observed 2026-08-04T21:26:01.837014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation ad468920-3942-4c9d-a5b2-e84718f84e6d · outbound

This paper cites & Gaissmaier, W.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions & Gaissmaier, W

Reference 22

Resolution
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raw_fallback, observed 2026-08-04T21:26:01.821140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 98eeeb73-16fe-4151-9f0e-3dfaa92a6d9e · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 23

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no resolver link, observed 2026-08-04T21:26:01.472592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T21:26:01.472592Z digest=sha256:9fa701729a0965a34f147aca7fba286be2f8705e588567646c98c947a137cb59

Observation aad6a5ce-bccc-4e78-b89e-957f198ae03c · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 24

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unresolved
raw_fallback, observed 2026-08-04T21:26:01.795367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 162fd47e-3b02-40bb-9c58-c1d135f33060 · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-08-04T21:26:01.782003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 928502d9-dd15-4b1c-8861-6c6d9e6c08d7 · outbound

This paper cites & Afzal, W.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions & Afzal, W

Reference 26

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raw_fallback, observed 2026-08-04T21:26:01.767579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 5047a31a-c48f-4121-93b9-52c5dff528ff · outbound

This paper cites & Jin, S.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions & Jin, S

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.753076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 8deab4dd-dabb-4ec5-8f08-239612e5d44b · outbound

This paper cites & Ouyang, X.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions & Ouyang, X

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.739454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 30c0dfa0-480b-4499-ad22-b1afe59be1b9 · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-04T21:26:01.725365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation f090609e-2cec-453a-8adf-05f98ce3dd29 · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 30

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unresolved
raw_fallback, observed 2026-08-04T21:26:01.710697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation c63261de-99b2-4222-a90b-5772d79b89ee · outbound

This paper cites C., Hillers, V.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions C., Hillers, V

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.695579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 6f7fb1c8-3e57-40ed-ba8b-6710f444e095 · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 32

Resolution
unresolved
raw_fallback, observed 2026-08-04T21:26:01.677769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.509988Z digest=sha256:b7d93aa448d1ebc1b3c682e3cd32545e85a2c28137c97eacd37880a0cb407213

Observation 3a62ffc2-7f1b-47e3-be0d-841d5f02628b · outbound

This paper cites an unresolved cited work.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions Unresolved cited work

Reference 33

Resolution
unresolved
raw_fallback, observed 2026-08-04T21:26:01.661843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.513889Z digest=sha256:d9f6bff281a6d5b6ec57cd0152eae93fc87abef9e859ca5bd96d6eb6e7f43aa8

Observation 0363e268-fef2-4c5d-b923-cc1bf5898a04 · outbound

This paper cites & Baranyi, J.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions & Baranyi, J

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.645700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.518315Z digest=sha256:79fc49b832c5dc35505e2c893049c510a4a20b613252dc59b2698d496e028403

Observation cbe901ca-3e8b-4755-9184-a7fccfff26e9 · outbound

This paper cites & Kim, J.

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions & Kim, J

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:26:01.630962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-04T21:26:01.522592Z digest=sha256:76d3510850d618773431831b00887f9de7e2c076ecb88e881e1eff572eca3f25

Pith citing papers

No inbound Pith citation observations are available.