Daily lying behaviour as an indicator of pregnancy toxemia in dairy goats
Bibliographic record
Abstract
When clinical signs such as lethargy and immobility are noted, the prognosis for recovery from pregnancy toxemia is poor. These behavioural changes likely occur progressively, and could therefore be used as early indicators of illness. The aim was to evaluate the value of monitoring lying behaviour to identify illness. Daily lying time and lying bout frequency were calculated for 12 d before kidding on 10 commercial dairy goat farms in Ontario, Canada. Does were monitored for ketonemia (elevated blood β-Hydroxybutyrate, BHBA), a factor associated with pregnancy toxemia. Does were considered healthy (n=232) when BHBA < 0.9 mmol/L before and after kidding, and ketonemic (n=14) when BHBA ≥ 1.7 mmol/L before kidding. PROC GLIMMIX (SAS) models were used to assess the effect of health status on lying time and lying bout frequency, with litter size as a covariate. Results presented as mean ± SED (lying time) and mean and 95% CI (lying bout frequency). Ketonemic does lay longer than healthy does (15.5 vs. 12.8 h/d, SED=0.9; P=0.002). Lying time decreased near kidding, but compared to healthy does, ketonemic does continued to lie down longer on the day before kidding (16.0 vs. 13.2 h/d, SED=1.1; P=0.02) and on kidding day (13.0 vs. 9.8 h/d, SED=1.1; P=0.005). Does carrying triplets tended to have longer lying times by 1.0 h/d (SED=0.3; P=0.07). There was no difference in lying bout frequency between ketonemic and healthy does; all does increased the number of lying bouts between the day before kidding and kidding day (16.8 (15.8–17.8) vs. 20.5 (19.4–21.8) bout/d; P<0.0001)). In summary, ketonemic goats continued to lie down around kidding time, a period normally associated with increased restlessness. Behavioural differences were noted throughout the 12 d monitoring period suggesting that at-risk goats could be identified well in advance of kidding using lying time monitoring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".