Does the climate influence the variance of residual in litter traits of Taiwan Landrace sows?
Bibliographic record
Abstract
Heat stress adversely affects the litter traits of Landrace, the primary dam breed in Taiwan. This study aimed to evaluate the effects of heat stress during sow pregnancy with homogeneous and heterogeneous residuals to estimate the genetic parameters of the Taiwan Landrace. Performance records for 11 657 litters and weather data from 2008 to 2021 were collected. The climate effect was defined proportionally from cool to hot for climates 1 to 5. The homogeneous residual analysis showed that the heritabilities of the total number born (TNB) and number born alive (NBA) were 0.164 ± 0.014 and 0.111 ± 0.014 with residual variances of 10.338 and 9.164, respectively. The heterogeneous residual analysis showed that the residual variances for TNB and NBA were 8.934–11.113 and 8.196–9.810, respectively. For TNB, the residual variance in the herd–year–climate effect differed significantly in climate 1 from climates 3 to 5 ( p < 0.01). In NBA, the residual variance was significantly lower in climates 1 and 2 than in climates 3 and 5 ( p < 0.01). In conclusion, heritability was estimated for TNB and NBA. In addition, residual variances could interact with the climate effect in heterogeneous residual analysis.
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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 | 0.000 | 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".