Retrospective Video Analysis of the Behavior of Periparturient Dairy Cattle
Bibliographic record
Abstract
The impact of calving difficulty on the behavior of periparturient dairy cattle has not been reported. The objective of this study was to measure variables associated with difficulty of calving, and to identify the associations of parameters with periparturient behavior. Previously recorded videotapes of 23 cows on day -1, 0 and +1 relative to calving were analyzed. Data included frequency and duration of standing and lying behavior, calving difficulty score, duration of calving and calf body measurements. Calving difficulty was scored as: 0-unassisted, 1-easy pull, 2-hard pull. Video information was analyzed, interpreted and entered. Data were analyzed using logistic and linear regression with Statistix. Heifers were more likely to require assistance during calving (P<0.01). Male calves weighed significantly more than female calves (P<0.05). Cow standing and lying behavior differed on the day of calving. Pre-calving, heifers lie for 18 fewer minutes per lying bout compared to cows (37.1 ± 3.8 versus 55.1 ± 6.4 minutes per bout, P<0.02). Mean pre-calving lying time was also influenced by calving difficulty, as cows requiring assistance spent 15 minutes less lying down per bout (P<0.05). Post-calving, heifers lie for 13 minutes less per bout than cows (40.4 ± 3.1 versus 53. 7 ± 8.8, P=0.09). Cows taking longer to calve lie for fewer minutes each bout (P<0.02). Finally, dams that had hard pulls lie for 12.5 fewer minutes each bout (P=0.11). When mean lying bouts post-calving was analyzed, controlling for pre-calving mean lying time, only calving duration remained significant. Calving duration also positively influenced the number of post-calving lying bouts (P=0.05). Lying behaviour, both pre and post-calving, was the most significant measurement related to calving. Cows with longer calvings spent less time lying down after calving. Differences between lying time for cows and heifers merits further investigation. It is possible that this difference is related to the presence of pain, and that there are opportunities for pain management at calving.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".