A path analysis of the factors associated with seasonal variation of breeding failure in sows
Bibliographic record
Abstract
Data pertaining to 868 904 services of sows from 58 Canadian herds for the period 1999 to 2003 were retrieved from the PigCHAMP data share database and subjected to path analysis to evaluate the effect of number of inseminations per service (1 and >1), wean to service interval (WSI ≤ 5 d and > 5 d) and parity (parity 1, parities 2 to 5 and parity > 5) on the seasonality of breeding failure. Population attributable fraction (PAF) was calculated to determine the contribution of each risk factor. Overall breeding failure proportions were 23.5 and 20.9% in summer and other months, respectively. The likelihood of breeding failure was higher when sows were artificially inseminated (AI) only once, and 6 and 5% of breeding failures in the summer and other months, respectively, were attributable to single insemination. The likelihood of breeding failure was 1.5 and 1.4 times higher when WSI was > 5 d in the summer and other months, respectively, and 14% of breeding failure in summer was attributable to increase in WSI in summer, and in other months it accounted for 10%. Parity 1 sows reduced the proportion of breeding failure in the population in months other than summer [odds ratio (OR) 0.78]. Sows of parity 2–5 reduced the proportion of breeding failure in both seasons (OR 0.81 and 0.78 in summer and other months, respectively). Key words: Sow, season, breeding failure, WSI, frequency of AI
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".