17 Effects of birth traits, physical or fenceline boar exposure and group size on pubertal measures and lifetime fertility of replacement gilts
Bibliographic record
Abstract
Abstract In experiment 1, prepubertal gilts with (n = 264) and without (n = 43) birth records received Fenceline (FBE) or Physical (PBE) Boar Exposure (BE) in a Boar Exposure Area (BEAR). At 185 d of age, gilts (13/pen) received BE for 15 min/d for 3 wk. At the start of Week 3, anestrual gilts received PG600 or no-PG600 (Control). At estrus, females were moved into stalls and inseminated at 2nd heat. Gilts born in larger litters were lighter (r = -0.26) while heavier pigs grew faster to puberty (r = 0.25). PBE increased estrus in Week 1 (38%) over FBE (28%). In Week 3, PBE-PG600 increased estrus (79.9%) compared with PBE- Control (36.2%), while FBE-PG600 and Control did not differ (52.7 vs. 42.5%). By 6 wk, estrus tended to be greater (P < 0.08) for PBE (91.2%) than FBE (83.2%). Reduced fertility associated with: 1) small birth litter; 2) heaviest birthweight; 3) slower growth rate; 4) delayed puberty and age at 1st service; and 5) abnormal estrus interval. Experiment 2 tested the pubertal response to PBE or FBE with 10 or 20 gilts/pen. Gilts (n = 180) at 168 d with 1.8 m2 floor space received BE once/d for 15 min for 1–3 wk. At the start of Week 3, anestrual gilts received PG600. Estrus in Week 1 (7.3%) did not differ, but a BE x Pen effect occurred in Week 2 (estrus range: 15–34%). In Week 3, PG600 increased estrus (P < 0.03) in Pens of 10 (83.7%) compared to Pens of 20 (64.1%). BE method had no effect and Pens of 10 had greater estrus (P = 0.05) than Pens of 20 (88.3 vs 75.8%). These results indicated that use of PBE, a BEAR, smaller group size, and PG600 can be used in combinations to enhance puberty induction. Birth and pubertal measures influenced service and farrowing rate, litter size, and age at removal.
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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 | 0.000 | 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 | 0.000 |
| 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".