MétaCan
Menu
Back to cohort
Record W2190705349

THE CVMA URGES THE VDD TO PUBLISH MAXIMUM RESIDUE LEVELS AND WITHDRAWAL PERIODS FOR VETERINARY DRUGS USED IN CANADA

2002· article· en· W2190705349 on OpenAlexaboutno aff
Gordon Dittberner, Suzanne Lavictoire

Bibliographic record

VenueEurope PMC (PubMed Central) · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsVeterinary DrugsVeterinary drugVeterinary medicineAgency (philosophy)MedicineLivestockBusinessPolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

The Canadian Veterinary Medical Association (CVMA) has received a flurry of calls and letters from concerned veterinary practitioners, livestock producers, processors, and various associations soliciting its support as Canadian federal agencies abruptly modified their procedures regarding the sampling and testing for veterinary drug residues in meat products. Tens of thousands of poultry carcasses had to be frozen to await additional residue test results because federal inspectors had no published Withdrawal Periods (WPs) and/or Maximum Residue Levels (MRLs) from Health Canada with which to make decisions. Some of the veterinary drugs implicated are approved and have established WPs and MRLs. Similar complaints have also come from beef and swine veterinary practitioners and producer associations. Examples were given of lesser withdrawal periods for the same drug used in animal and meat products in the United States and allowed into the Canadian markets. An examination of the facts made it clear that the problems were not confined to one particular product, or to one particular species or interest group. Corrective action had to be taken. As a first step, the CVMA has written to the Veterinary Drugs Directorate urging action on this issue. It is CVMA's understanding that the Canadian Food Inspection Agency has already undertaken some remedial action and has developed draft policy changes. (by Dr. Gordon Dittberner, Senior Advisor, Veterinary Affairs, and Suzanne Lavictoire, Director, Programs)

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.200
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0040.002
Scholarly communication0.0070.002
Open science0.0030.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0410.021

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 head0.028
GPT teacher head0.204
Teacher spread0.176 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueEurope PMC (PubMed Central)Same topicPesticide Residue Analysis and SafetyFrench-language works237,207