Mortality and Survival Probability of Female Pigs in Southern European Commercial Breeding Herds
Bibliographic record
Abstract
This retrospective cohort study characterized death occurrences in female pigs in commercial herds, examined the survival probability for served females, and quantified factors associated with by-parity mortality risks for farrowed sows. The cohort data from herd entry to removal included 558,486 first service records of 113,517 females in 121 herds, served between 2008 and 2013. Two herd categories were defined on the basis of the lower 25th percentiles of herd means of annualized lifetime pigs weaned per sow: low-performing herds and ordinary herds. Two-level survival analysis was performed for served females to obtain by-parity survival probabilities (gilts and parity 1-6 sows). Also, log-binomial regression models were used to examine risk factors and ratios associated with mortality risks for the seven parity groups farrowed in either spring, summer, autumn or winter. Overall mean mortality incidence rates were 1.22 and 5.30 pigs per 100 pig-days of observation for pregnant females and farrowed sows, respectively. Survival probabilities for served females rapidly decreased by 2.3%-3.7% around farrowing in all parity groups. The lower survival probabilities were associated with increased age at first-mating, females in low-performing herds, sows farrowing more stillborn piglets and sows having weaning-to-first-mating interval of 7 days or more (P < 0.05). Mortality risks were assessed for herds, served month, WMI and stillborm piglets. Increased risks were associated with summer farrowing, low-performing herds and more stillborn piglets (P < 0.05). We recommended that producers individually monitor high risk female groups, and provide prompt treatment to any females with problems.
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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) | 0.001 | 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".