Bibliographic record
Abstract
The present study was undertaken to investigate the reproductive performance of the female breeding pigs after artificial insemination (AI) using the frozen boar semen imported from Canada, thereby finding insights into improving the efficiency of AI using the frozen semen (FSAI). Analyzed in the present study were the records of a total of 626 FSAI in a great grandparent (GGP) farm beginning from May through November of the year of 2016 (Farm A) and 2,024 FSAI beginning from 2015 through 2017 from a second GGP farm (Farm B). Both the total number of piglets born (TNB) and the number born alive (NBA) were greater during May than during September within FSAI (p<0.05) in Farm A (p<0.01 for the effect of the month). In Farm B, no difference was detected between the years in any of the farrowing rate, TNB, and NBA. When the records from Farm A and Farm B were pooled, the farrowing rate was greater for Farm A vs. Farm B (p<0.01), with no difference between the two farms in TNB and NBA. Moreover, TNB and NBA were less for FSAI than for AI using the liquid semen (LSAI; 10.9±0.3 vs. 13.4±0.1 and 10.0±0.3 vs. 12.0±0.1 piglets, respectively, for FSAI vs. LSAI in TNB and NBA, respectively; p<0.01). In conclusion, these results suggest that the reproduction efficiency for FSAI, which is lower than that for LSAI, could be improved by selecting an optimal period of the year for the use of the former.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".