Bibliographic record
Abstract
In recent years, the use of artificial insemination (AI) technologies has dramatically increased in the United States swine industry. However, relatively little literature is available regarding AI boar production performance. Kennedy, et al.,1 evaluated 1970s collection data from a Canadian boar stud and reported that month, collection interval, and boar age all had effects on productivity. Potential doses were highest from November–January and lowest from April–June. Boars 24–29 months of age generated the most potential doses; boars < 8 months of age generated the fewest. Percent live sperm and motility were highest for young boars and decreased with age. Kemp, et al.,2 conducted a prospective study to evaluate the effect of collection frequency on production. They concluded that only a short-run gain in sperm production was achieved by collecting boars at a higher frequency—five times per 2 weeks instead of three times per 2 weeks. In a different prospective study, Cameron3 concluded that daily sperm production was greatest with 24-hour collection intervals; however, libido among those boars decreased toward the end of the study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".