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Record W2418761899

Survey of Saskatchewan beef cattle producers regarding management practices and veterinary service usage.

2015· article· en· W2418761899 on OpenAlexaffabout
Murray Jelinski, John Campbell, Steven Hendrick, Cheryl Waldner

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHerdAnimal healthVeterinary medicineAgricultural scienceMedicineBusinessGeographyBiology
DOInot available

Abstract

fetched live from OpenAlex

Saskatchewan cow-calf producers (n = 2000) were surveyed to determine what factors were associated with their uptake of veterinary services; how and where they access nutritional information and animal health advice; and whether they were comfortable with having non-veterinarians perform veterinary procedures. The survey response rate was 18.1%. Veterinarians were seen as a primary source of nutritional information and animal health advice. Over the past decade producers have shifted their veterinary service usage from individual animal events to herd-level procedures. Producers who pregnancy check were more likely to be large producers (OR = 1.9; 95% CI = 1.2 to 3.1; P = 0.007), to semen test their bulls (OR = 3.4; 95% CI = 2.0 to 5.8: P < 0.001), analyze their forages (OR = 2.3; 95% CI = 1.7 to 4.0; P = 0.006), and to farm in the brown versus the gray or dark brown soil zones (P = 0.004). Most (94.0%) respondents had adequate veterinary services within an hour's drive of the farm and 90.4% were satisfied with their veterinary service provider. Approximately 25% of respondents would be comfortable with having a non-veterinarian pregnancy check and attend to prolapses.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.086
GPT teacher head0.251
Teacher spread0.165 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations15
Published2015
Admission routes2
Has abstractyes

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