Table_4_The Canadian Cow-Calf Surveillance Network – productivity and health summary 2018 to 2022.pdf
Bibliographic record
Abstract
Cow-calf surveillance data provide critical information about changing herd demographics, productivity, health, and management as well as the opportunity to investigate the impact of differing management practices. A national cow-calf network was established to collect baseline information on herd health, production performance measures, and management. Questionnaires were used to collect information on herd attributes as well as data for the calving season, breeding, pregnancy testing, and weaning for herds from across Canada. From 2018 to 2022, a total of 565 calving record questionnaires, representing 110,658 calving female records from 171 herds were returned, along with 543 herd breeding to weaning questionnaires. Suggested benchmark values based on the 25th percentiles from Western and Eastern Canada were determined to be <5% for non-pregnancy, <2% for calf death from birth to 24 h, and < 2% for calf death from 24 h to weaning. Herds from Eastern Canada were more likely to report any assistance at calving (p < 0.001) than herds from Western Canada. Participating herds from the east had longer breeding (p < 0.001) and calving (p < 0.001) seasons than those from the west and were also more likely to use artificial insemination or embryo transfer (p < 0.001). Timing of calving season and use of large pastures for calving were not associated with mortality at birth. Herds that started calving before April were more likely to have calves die before 30 d than those that started calving later; however, this difference was not observed between 30 d and weaning. Herds that started to calve earlier were also more likely to report treating more calves for bovine respiratory disease, diarrhea, and navel or joint infections as well as to calf deaths from respiratory disease. Calves from herds in the east were more likely to be treated or die from diarrhea than from the west. Females from herds that started calving later were less likely to be pregnant. The unique longitudinal productivity and health data resulting from this network established a national baseline to address region-specific needs for knowledge translation and solutions to enhance productivity and support sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.285 | 0.028 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".