Nurse Sows’ Reproductive Performance in Different Parities and Lifetime Productivity in Spain
Bibliographic record
Abstract
Our objective was to characterize use of nurse sows in Spanish breeding herds. We analyzed 466 111 parity records and lifetime records of 92 716 sows farrowed between 2011 and 2017 in 69 herds having nurse records. Nurse sows were defined as sows that had weaned 2 or 3 litters in the same lactation period. Mixed-effects models were applied to the data to compare reproductive performance and lifetime productivity between nurse and non-nurse sows. Of all the sows, 6 705 (7.2%) sows served as nurse sows at least once in their lifetime, with 10.2% of the nurse sows having a second nurse event in a later parity. Mean values (SE) of lactation length and number of piglets weaned were 31.0 (0.11) days and 21.9 (0.04) piglets in nurse sows, respectively. Across parities 2-6, nurse sows had 1.9-3.0% greater proportions of weaning-to-first-mating interval 7-20 days than non-nurse sows (P < 0.05). There was no difference between nurse sows and non-nurse sows in farrowing rate in any parity (P ≥ 0.13) and piglets born alive in parities 1-5 (P ≥ 0.15). Also, nurse sows had 3.7-7.4 more annualized lifetime piglets weaned than non-nurse sows (P < 0.01), because nurse sows had similar lifetime non-productive days with non-nurse sows (P ≥ 0.07), but produced 9.3-12.0 more lifetime piglets weaned than non-nurse sows (P < 0.01). Using nurse sows could be a good practice to cope with highly prolific sows.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".