Accounting for lactation length and weaning-to-conception interval in genetic evaluations for litter size in swine.
Bibliographic record
Abstract
Effects of lactation length and weaning-to-conception interval on the subsequent litter size of purebred sows were estimated using an animal model. Data on 2,847 Landrace sows with 7,125 litters born between January 1989 and May 1997 and on 1,234 Yorkshire sows with 2,999 litters born between January 1990 and May 1997 were obtained from two Canadian selection herds. Sows having a lactation of less than 14 d (MMEW) were usually not mated until their second estrus, whereas sows weaned after at least 14 d of lactation (later weaning) were usually mated on their first estrus. Litter size included both number of pigs born alive and those stillborn. Linear, quadratic, and logarithmic effects of lactation length were tested. The effect of weaning-to-conception interval on litter size was modeled using an approach based on threshold variables and an approach using segmented polynomials. Results indicated linear and logarithmic effects of lactation length on subsequent litter size for Yorkshire and Landrace breeds, respectively. Litter size decreased as weaning-to-conception interval increased up to 7 and 10 d for Yorkshire and Landrace, respectively, then increased with further increases in weaning-to-conception interval up to 35 and 30 d for the two breeds, and then remained constant. The MMEW sows did not have lower subsequent litter sizes than later-weaned sows because the negative effect of a shorter lactation was offset by the positive effect of a longer weaning-to-conception interval. However, average time spent open per parity was longer for MMEW sows than for later-weaned sows. Both lactation length and weaning-to-conception interval should be considered in models for the genetic evaluation of litter size in purebred swine. Segmented polynomials can be used to predict litter size as a continuous function of weaning-to-conception interval or to derive weaning-to-conception interval adjustment factors for litter size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".