Effects of Training for First Milking Involving Positive Tactile Stimulation on Post-Calving Maternal Behaviors in Primiparous Gyr Dairy Cows
Bibliographic record
Abstract
In dairy systems with Zebu breeds, calves are not immediately separated from their dams after calving; consequently, maternal care and protective behavior are important, influencing both productive performance and stockpeople’s safety. Our objectives were to: (1) investigate the effects of a training protocol involving pre-calving positive stimulation, delivered prior to calving, on the maternal care of primiparous Gyr cows; and (2) evaluate the effects of this training protocol on maternal protective behavior towards handlers during the first calf handling. Primiparous dairy Gyr cows (n = 37) were allocated into two groups: training (n = 16) and control (n = 21). Animal behaviors were recorded in three periods: post-calving, first calf handling, and post-handling. Maternal protective behavior during calf handling was assessed from measures of aggressiveness, attention, displacement, and agitation. Calf latency to stand up (p < 0.01) and sex (p < 0.01) differed between the training and control groups. The training group had less touching (p = 0.03), more time not interacting with the calf (p = 0.03), tended to be less protective (p = 0.056), and moved less (p < 0.01) during the first handling of their calves. In conclusion, the primiparous dairy Gyr cows subjected to pre-calving training protocol displayed less maternal care and displacement during the first handling of their calves and tended to be less protective.
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 | 0.000 |
| 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".