Paper Citation Record · LEDGER

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning

As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2509.03551.

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

pith.paper-citation-record.v1
2509.03551 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:20:10.514816Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

36 of 36 outbound references displayed

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verified fuzzy36
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 39bce0e0-4578-4c20-944d-ce8777204aad · outbound

This paper cites Antimicrobial resistance: Global report on surveillance.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Antimicrobial resistance: Global report on surveillance

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.839943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 585b26a8-8ee9-43f5-8508-5629ca9dbb10 · outbound

This paper cites Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.831138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b09a9d1f-9489-42c3-947e-7014f019cdbb · outbound

This paper cites The European Union summary report on antimicrobial resistance in zoonotic and indicator bacteria from humans, animals, and food in 2021/2022.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning The European Union summary report on antimicrobial resistance in zoonotic and indicator bacteria from humans, animals, and food in 2021/2022

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.822429Z

Source-reported events for the cited work

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Observation 5723be76-d43e-44c0-93d4-a7ce116e1d39 · outbound

This paper cites A survey of Campylobacter in the UK poultry supply chain.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning A survey of Campylobacter in the UK poultry supply chain

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.813962Z

Source-reported events for the cited work

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Observation e9af6409-6d13-4ec5-a5cd-02f1b1d5558e · outbound

This paper cites The economic burden of Campylobacter-associated Guillain-Barr´ e syndrome in the UK.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning The economic burden of Campylobacter-associated Guillain-Barr´ e syndrome in the UK

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.805373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 327866ac-8a7d-4d6f-b290-aaaa607f13c4 · outbound

This paper cites Mechanisms of antimicrobial resistance in Campylobacter.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Mechanisms of antimicrobial resistance in Campylobacter

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.797291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 76a31635-859b-456f-ae61-0ee91ac4c2bd · outbound

This paper cites Computer age statistical inference: Algorithms, evidence, and data science.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Computer age statistical inference: Algorithms, evidence, and data science

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.788859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20167f1f-a010-47d5-9666-a943976262cb · outbound

This paper cites Machine learning approaches for predicting antimicrobial resistance in nontyphoidal Salmonella.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Machine learning approaches for predicting antimicrobial resistance in nontyphoidal Salmonella

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.780621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bf051cdc-a930-49e7-8474-212474d2e793 · outbound

This paper cites Prediction of antibiotic resistance in Escherichia coli from whole-genome sequences.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Prediction of antibiotic resistance in Escherichia coli from whole-genome sequences

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.772651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b361153e-b2c9-4ef2-8efa-6ad91f766ebb · outbound

This paper cites Emergence and evolution of multidrug- resistant Campylobacter jejuni sequence type 5136 in the UK.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Emergence and evolution of multidrug- resistant Campylobacter jejuni sequence type 5136 in the UK

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.764088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3ce10e37-e85c-4180-9132-4afeccfc4384 · outbound

This paper cites DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.755166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 97792d0e-24ff-47ec-a055-79a8f8933a2a · outbound

This paper cites Population-level mathematical modeling of antimicrobial resistance: A systematic review.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Population-level mathematical modeling of antimicrobial resistance: A systematic review

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.747018Z

Source-reported events for the cited work

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Observation 6d0355a8-00bd-4d4a-8f6c-c883287b45a3 · outbound

This paper cites Another look at forecast-accuracy metrics for intermittent demand.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Another look at forecast-accuracy metrics for intermittent demand

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.738803Z

Source-reported events for the cited work

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Observation c966a63c-20b7-45a5-85fb-90caf375c09e · outbound

This paper cites The true cost of antimicrobial resistance.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning The true cost of antimicrobial resistance

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.730839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e4fdfc6-24a3-4ba6-89f0-73de39557e66 · outbound

This paper cites Quantifying the economic cost of antibiotic resistance and the impact of related interventions.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Quantifying the economic cost of antibiotic resistance and the impact of related interventions

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.722606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 984a7176-3478-4d08-8db5-418b64ec85ea · outbound

This paper cites Tackling drug-resistant infections globally: Final report and recommendations.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Tackling drug-resistant infections globally: Final report and recommendations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.714027Z

Source-reported events for the cited work

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Observation d330613d-2efe-41c2-b850-64c546b10b79 · outbound

This paper cites Integrated Campylobacter surveillance.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Integrated Campylobacter surveillance

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.706302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c6b7cd6f-e8de-4b4b-be0a-d7e8cbd62c20 · outbound

This paper cites Campylobacter attribution study.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Campylobacter attribution study

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.698272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7eff380d-97b7-45bb-9112-44cd568e0e85 · outbound

This paper cites Quinolone resistance in Campylobacter: Mechanisms and epidemiology.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Quinolone resistance in Campylobacter: Mechanisms and epidemiology

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.690163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d6d37117-771a-46de-bb1b-806ad2f3bc25 · outbound

This paper cites Beta-lactam resistance mechanisms in Campylobacter.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Beta-lactam resistance mechanisms in Campylobacter

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.681841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 581c35e9-2a2e-43e3-9bb1-d6c2f2464588 · outbound

This paper cites Feature engineering and selection: A practical approach for predictive models.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Feature engineering and selection: A practical approach for predictive models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.673167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 85281e88-0c87-4a70-847f-2ebbda6ccded · outbound

This paper cites Forecasting: Principles and practice.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Forecasting: Principles and practice

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.664917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation def1dced-d039-495b-a9cc-c09a66171289 · outbound

This paper cites Forecasting at scale.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Forecasting at scale

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.656965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c9cc610-7158-460b-9bbc-f0ebe79364f3 · outbound

This paper cites Scikit-learn: Machine learning in Python.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Scikit-learn: Machine learning in Python

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.648808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6cccaa1a-02f2-489c-bf4d-69c5b7360074 · outbound

This paper cites Fundamentals of data visualization.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Fundamentals of data visualization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.640444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d6e7a2d9-ed07-4859-a992-4b6d0348a1e1 · outbound

This paper cites The F AIR Guiding Principles for scientific data management and stewardship.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning The F AIR Guiding Principles for scientific data management and stewardship

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.632235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2bbf1d78-1af3-4847-a8c8-999ee34a82ab · outbound

This paper cites Emerging resistance mechanisms in Campylobacter.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Emerging resistance mechanisms in Campylobacter

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.623359Z

Source-reported events for the cited work

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Observation 0161715f-c6d4-43e8-81f4-b15b222592bb · outbound

This paper cites Antimicrobial resistance in the EU/EEA.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Antimicrobial resistance in the EU/EEA

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.615364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f6db9e4-1fdc-4c1d-b7d0-e940e77d0d05 · outbound

This paper cites Global antimicrobial resistance and use surveillance system (GLASS) report.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Global antimicrobial resistance and use surveillance system (GLASS) report

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.607230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e873b868-786c-4d2b-bc94-c780cabee19f · outbound

This paper cites Antibiotic resistance: Are we all doomed? Internal Medicine Journal , 2016.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Antibiotic resistance: Are we all doomed? Internal Medicine Journal , 2016

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.598783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec61acef-6236-4d94-bd43-9d50aa74052b · outbound

This paper cites Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.589540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b0f3373-86d6-4724-b75a-15b6f3237b52 · outbound

This paper cites A unified approach to interpreting model predictions.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning A unified approach to interpreting model predictions

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.580046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1e599afe-096c-4732-9d8e-04005b5ab8dc · outbound

This paper cites Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.571552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0150dd20-3e37-4ebf-9805-0e58e1670985 · outbound

This paper cites Restricting the use of antibiotics in food- producing animals and its associations with antibiotic resistance.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Restricting the use of antibiotics in food- producing animals and its associations with antibiotic resistance

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.562577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ee36707c-828b-4fd9-a03a-ad5f71120b9a · outbound

This paper cites Rapid detection of antimicrobial resistance using genomic approaches.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Rapid detection of antimicrobial resistance using genomic approaches

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.553920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c79e8e1-9f41-4b7c-9759-408abdf5a6d7 · outbound

This paper cites Understanding the mechanisms and drivers of antimicrobial resistance.

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Understanding the mechanisms and drivers of antimicrobial resistance

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:20:10.544351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.