Characteristics of estrous behaviour in heifers and lactating cows and its associations with fertility
Bibliographic record
Abstract
Detection of estrus in dairy cows is challenging, partly because of poor behavioural expression. Automated activity monitors allow quantification of estrus expression based on restlessness. The main goals of this thesis were to use automated measurements and visual observation of behaviour to increase understanding of estrus characteristics, variation among animals, risk factors for poor expression, and its association with fertility. In the first study, the behaviour of heifers was video-recorded and activity peaks were identified from accelerometer data; estrus was validated by ovarian ultrasonography. Chin rest, sniff, back mount, crossover, and follow had the largest increase in frequency during estrus. Estrus relative increase in walking activity (290 ± 160%) and duration (14 ± 4 h) varied greatly and were affected by estrus order, season and time of the day. The second study investigated how estrus affected automated measurements of lying and standing behaviour, a less explored aspect of estrus. At estrus, bout frequency was lower, daily standing time was greater, and heifers stood uninterruptedly for twice longer than at baseline. Relative changes in standing behaviour at estrus were smaller for estrus starting between 1200 h and 0300 h. The third experiment investigated the agreement between estrus characteristics in heifers fitted with two accelerometers. Both systems were precise (PPV = 84.7% [Heatime] and 98.7% [IceTag]) and provided similar characterization and timing. Plasma estradiol was not correlated with follicle diameter, duration, intensity, or presence of estrus signs. Finally, estrus lying behaviour of lactating cows and its associations with fertility were studied. Daily lying time and bout frequency were reduced at estrus (65 ± 21% and 65 ± 24% of baseline). Ovulation and pregnancy at d 32 after AI were 4.9 and 1.6 times more likely if estrus lying time was < 75% of baseline. Collectively, results suggest potential application of lying behaviour towards fertility prediction. We have also highlighted features such as variability, risk factors and basal activity that can contribute to assessment of methods and practices for increased expression. Additional future directions include investigation of physiological bases of estrus-fertility association and real-time applications of characterization data.
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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 | 0.001 |
| 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".