Factors for Culling Risk due to Pregnancy Failure in Breeding-Female Pigs
Bibliographic record
Abstract
A high occurrence of culling due to pregnancy failure disturbs the flow of pig production and decreases productivity of female pigs in commercial breeding herds. The objective of the present study was to quantify changes in culling risk due to pregnancy failure [CRPF] in breeding-female pigs due to variation in outdoor climate and production factors. The data included 240 143 parity records and 54 858 lifetime records of female pigs in 99 commercial herds located in humid subtropical and continental climate zones. The weather data were acquired from 21 local meteorological observatories close to the studied herds. Mean daily average temperature [Tmean] and relative humidity [RH] during the 21-day pre-mating period for each female were matched with the female’s reproductive data. Generalized linear mixed-effects models were applied to the data. Mean by-parity and lifetime CRPF (± SE) were 3.6 ± 0.04% and 20.1 ± 0.17%, respectively. An increase in by-parity CRPF was associated with higher Tmean, higher gilt age at first-mating, fewer pigs born alive and prolonged weaning-to-first-mating interval (P < 0.05), but not with mean RH (P > 0.18) or weaning age (P ≥ 0.31). For instance, CRPFs for female pigs in parities 0-5 were 1.5-2.4% higher at Tmean 30 °C than at 10 °C (P < 0.05). Therefore, producers are recommended to closely monitor at-risk female pigs and apply advanced cooling equipment to reduce heat stress, and provide appropriate management to prevent increased CRPF in female pigs.
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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 | 0.002 |
| 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) | 0.001 | 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".