Reproductive performance with an automated activity monitoring system versus a synchronized breeding program
Bibliographic record
Abstract
The objective was to compare reproductive performance with management programs based on an automated activity monitoring (AAM) system or a synchronized breeding program under field conditions. In total, 1,429 Holstein cows from 3 commercial herds in Ontario, Canada, were enrolled over 1 yr in a randomized controlled trial. At each farm, primiparous and multiparous animals were housed in separate pens. At the pen level, cows were assigned to reproductive management primarily using an AAM system based on monitoring activity levels (Heatime, SCR Engineers Ltd., Netanya, Israel) or a timed artificial insemination (TAI) program. A crossover occurred after 6 mo of the trial to avoid confounding treatment with parity. Insemination based on additional detection of estrus by visual observation was practiced in all pens. At the individual cow level, time to pregnancy throughout the study (n=1,985 cow-6 mo periods), time to first service, and time to second service were analyzed with a Cox proportional hazards models accounting for herd, and did not differ overall between the AAM and TAI treatment groups. However, we observed an interaction between herd and breeding program, such that association of AAM- or TAI-based program with these outcomes differed between farms. Time to pregnancy was not different in herd A [median=151 and 136 d; hazard ratio (HR) = 0.93] and herd C (median=99 and 124, HR = 1.24), whereas herd B had a median time to pregnancy of 119 d and 146 d (HR = 1.3) in the AAM and TAI groups, respectively. Under conditions in which 19 to 32% of artificial inseminations in both groups were based on visually detected estrus, herd pregnancy rate and cow-level time to pregnancy did not differ overall between TAI- and AAM-based programs, but the effect of reproductive management approach depended on herd. Considering 924 cow-6 mo periods with artificial inseminations only by the assigned program, we also found an interaction between herd and breeding program. Stratified analysis showed no difference in time to pregnancy in herd A (HR = 1.3), whereas in herds B (HR = 1.7) and C (HR = 2.8), cows in the AAM treatment group became pregnant sooner compared with those in the TAI group. Factors that influence the variability in relative performance of these management systems between herds require further investigation. Our study used one commercial activity monitoring system and the results cannot necessarily be generalized to other systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".