Behavior, Displacement and Pregnancy Loss in Pigs under an Electronic Sow Feeder
Bibliographic record
Abstract
Our objective was to characterize eating behavior associated with displacement hazard and subsequent performance for pigs were fed in static groups by an electronic sow feeder (ESF). Data included weekly eating records and subsequent farrowing records of 685 pigs. The eating behavior comprised weekly averages of daily feed dispensed (ADFD) and daily total time spent in the feeding stations (TTSF). A displacement female was defined as a pig removed from her group for health reasons. A multivariate model and piecewise exponential models were fitted to the records. Means (inter-quartile ranges) of ADFD and TTSF were 2.4 kg (2.1-2.8 kg) and 9.3 min (7.5-10.8 min), respectively. Gilts had less ADFD than sows during gestational weeks 5-13 (P < 0.05), but there was no difference in TTSF between gilts and sows in gestational weeks 5-8 and 11-13 (P > 0.05). Also, gilts had higher displacement hazard than parity 2 or higher sows in gestational weeks 8-10 (P < 0.05). Pigs that were entered into the ESF system during summer had less ADFD, and shorter TTSF from gestational weeks 5 to 12 than those entered during the other seasons (P < 0.05). The TTSF varied between two genotypes during gestational weeks 5-7 (P < 0.05). Also, a higher displacement hazard was associated with less ADFD (P < 0.01). A higher hazard of pregnancy loss was associated with shorter TTSF (P < 0.01). In conclusion, we recommend that both ADFD and TTSF should be measured in ESF systems to help identity females having problems.
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 | 0.000 | 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 | 0.000 | 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".