Use of recorded nursing grunts during lactation in two breeds of sows. I. Effects on nursing behaviour and litter performance
Bibliographic record
Abstract
The impact of exposing lactating sows and their litters to recorded sow nursing grunts played at different intervals during lactation was studied. Yorkshire × Landrace (YL) and 25% Meishan (MH) primiparous sows were divided into three groups (n = 14): (1) no playback, (2) playbacks at 35-min intervals (GR35), and (3) playbacks at 40-min intervals (GR40). Recordings were played from day 110 of gestation to day 27 of lactation. Nursing behaviours, incidence of nursings without milk ejection (NPN), nursing interval and proportion of nursings induced by playbacks were measured on days 6, 18 and 26 of lactation. Litter size was standardized to 10 ± 1 piglets within 48 h of birth and piglets were weighed weekly. Mean nursing intervals, excluding NPN, were shorter for MH than for YL sows (P < 0.001). The increase in mean nursing interval between days 6 and 18 was greater in GR40 than in GR35 or controls (P < 0.01) and, when excluding NPN, the mean nursing interval decreased in GR35 on day 18 (P = 0.01). The occurrence of NPN decreased as lactation advanced (P < 0.001) and was lower for MH than YL sows on day 26 (P < 0.001). Between days 6 and 18, the proportion of nursings initiated by playbacks increased (P < 0.05) and the duration of milk ejection decreased (P < 0.001). In MH sows, controls had longer milk ejections than GR35 (P < 0.05) whereas, in YL sows, controls had shorter milk ejections than GR40 (P < 0.05) and GR35 (P = 0.06). Piglet growth was not affected by treatments or breed (P > 0.1). In conclusion, exposing sows and their litters to recorded sow nursing grunts played at 35-min intervals reduced nursing intervals on day 18 of lactation only, without affecting piglet performance. Key words: Auditory stimulus, behaviour, lactation, litter performance, Meishan, sows
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".