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Record W648183120 · doi:10.1002/9781444302639

Guide to Antimicrobial Use in Animals

2008· book· en· W648183120 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsnot available
Fundersnot available
KeywordsAntimicrobialBiologyComputational biologyMicrobiology

Abstract

fetched live from OpenAlex

Foreword: David Lloyd (Royal Veterinary College). Preface. Table of Contents. List of contributors. 1. Principles of Prudent and Rational Antimicrobial Use in Animals: Luca Guardabassi (University of Copenhagen) and Hilde Kruse (National Veterinary Institute Norway). 2. Human Health Risks Associated with Antimicrobial Use in Animals: Lars B. Jensen (Technical University of Denmark), Frederick J. Angulo (Centers for Disease Control and Prevention, USA), Kare Molbak (Statens Serum Institut) and Henrik C. Wegener (Technical University of Denmark). 3. Antimicrobial Resistance Risk Assessment: Emma Snary (Veterinary Laboratories Agency, UK) and Scott McEwen (University of Guelph). 4. Clinical Importance of Antimicrobial Drugs in Human Medicine: Peter Collignon (Australian National University), Patrice Courvalin (Institut Pasteur) and Awa Aidara-Kane (World Health Organization). 5. Geographical Differences in Market Availability, Regulation and Use of Antimicrobial Products: Angelo A. Valois (Australian Government Department of Agriculture Fisheries and Forestry), Yuuko S. Endoh (Ministry of Agriculture, Forestry and Fisheries (MAFF), Tokyo, Japan), Kornelia Grein (European Medicines Agency) and Linda Tollefson (US Food and Drug Administration). 6. Strategies to Minimize the Impact of Antimicrobial Treatment on the Selection of Resistant Bacteria: Peter Lees (Royal Veterinary College), Ove Svendsen (University of Copenhagen) and Camilla Wiuff (Health Protection Scotland). 7. Guidelines for Antimicrobial Use in Swine: David G. S. Burch (Octagon Services Ltd), C. Oliver Duran (Moss Veterinary Partners) and Frank M. Aarestrup (Technical University of Denmark). 8. Guidelines for Antimicrobial Use in Poultry: Ulrich Loehren (Lohmann & Co.), Antonia Ricci (Istituto Zooprofilattico Sperimentale delle Venezie) and Timothy S. Cummings (Mississippi State University). 9. Guidelines for Antimicrobial Use in Ruminants: Peter D. Constable (Purdue University), Satu Pyorala (University of Helsinki) and Geoffrey W. Smith (North Carolina State University). 10. Guidelines for Antimicrobial Use in Horses: J. Scott Weese (University of Guelph), Keith Edward Baptiste (University of Copenhagen), Viveca Baverud (National Veterinary Institute Sweden) and Pierre-Louis Toutain (Ecole Nationale Veterinaire). 11. Guidelines for Antimicrobial Use in Dogs and Cats: Luca Guardabassi (University of Copenhagen), G. Houser (University of Copenhagen), L. A. Frank (University of Tennessee) and M. G. Papich (North Carolina State University). 12. Guidelines for Antimicrobial Use in Aquaculture: Peter R. Smith (National University of Ireland, Galway), Alain Le Breton (Fish Consultant), Tor Einar Horsberg (Norwegian School of Veterinary Science) and Flavio Corsin (Ministry of Fisheries, Vietnam). Acknowledgements

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.253
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations189
Published2008
Admission routes1
Has abstractyes

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