Analysis of periparturient risk factors affecting sow longevity in breeding herds
Bibliographic record
Abstract
The association of periparturient risk factors with sow longevity and the validity of sow removal decisions made during the periparturient period were analyzed. Data pertaining to 2066 sows from a commercial breeding herd from the US Midwest were used in this study. The likelihood of removal from the herd within 35 d post-farrowing decreased with a younger parity, the absence of lameness or other health problems, a higher lactation feed intake (LFI) and a greater number of live-born piglets (P < 0.05 for all). A greater number of piglets born alive, the absence of lameness and a younger parity lowered (P < 0.05 for all) the likelihood of removal of sows from the herd before the next parity. The number of piglets born alive was higher (P < 0.05) among sows without any health problems during the previous periparturient period. A greater (P < 0.05) number of sows that were retained without any health problems during the periparturient period farrowed. More sows (P < 0.05) retained with health problems during the periparturient period were culled compared with sows retained without health problems during the periparturient period. In summary, periparturient factors such as LFI, the incidence of lameness or health problems, as well as sow-level characteristics such as higher parity and fewer piglets born alive predicted the removal of a sow from the breeding herd. Sows retained with periparturient health problems had reduced longevity and fewer live-born piglets, and fewer such sows had another farrowing. Key words: Longevity, sow, risk factor, feed intake
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".