Competition for Teats and Feeding Behavior by Group-Housed Dairy Calves
Bibliographic record
Abstract
Many farms using teat-based systems for supplying milk for calves provide only one or a small number of teats for a group of calves, but no previous research has addressed how competition for teats affects calf behavior or milk intake. The aim of this study was to determine how restricted access to teats affects calf competitive behavior, meal-based feeding patterns, and milk intake. Female calves (n=15) were divided into 5 groups of 3 calves each and fed with a teat-to-calf ratio that varied daily from 1:3 to 4:3 using a switchback design. Feeding behavior was recorded by scoring the time and duration of each sucking event. We defined meals using the frequency distribution of log intervals between visits to the teat and identified the within-meal and between-meal distributions intersection points. Three classes of intervals were identified based on the intersection points of the distributions: 1) intervals <2 min, representing small breaks away from the teat within a meal; 2) intervals >41 min, providing an objective definition of a new meal; and 3) an intermediate distribution of intervals from 2 to 41 min could be included in either of the other 2 classes. Meal number showed no significant decrease with decreasing teat number. However, total time on the teat decreased from 40.2 to 32.7 (+/-2.6) min/d, and milk consumption declined from 14.0 to 11.4 (+/-0.8) L/d as teat number declined from 4 to 1. In addition, competitive interactions became more frequent when teat access was reduced; the number of times calves displaced one another from a teat increased from 18 to 41 (+/-5) times/d when teat number decreased from 4 to 1. In conclusion, reduced access to teats increases competitive interactions, decreases feeding time and decreases milk intake by group-housed calves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".