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Record W6894263009 · doi:10.5683/sp3/vadxrc

Veterinarian perceptions on the care of surplus dairy calves

2022· dataset· en· W6894263009 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsThematic analysisFocus groupColostrumDairy farmingValue (mathematics)PerceptionDairy industry

Abstract

fetched live from OpenAlex

This study investigated the perspectives of Canadian cattle veterinarians on the care and management of surplus calves, as well as how they view their role in improving care. We conducted 10 focus groups with a total of 45 veterinarians from 8 provinces across Canada. Recorded audio files were transcribed, anonymized, and coded using thematic analysis. We found that veterinarians approached surplus calf management issues from a wide lens, with 2 major themes emerging: (1) problematic aspects of surplus calf management, including colostrum management, transportation, and euthanasia, and suggested management and structural solutions, including ways to improve the economic value of these calves, and (2) the veterinarian’s role in advising dairy farmers on the care of surplus calves, including on technical issues, and more broadly working with farmers to better address public concerns.

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)0.007
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.035
GPT teacher head0.297
Teacher spread0.262 · 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
GenreDataset

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
Published2022
Admission routes2
Has abstractyes

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