Predicting calving curves for herds using controlled breeding programs
Bibliographic record
Abstract
Abstract A database of gestation lengths (GLs) was generated over 2 years in three large dairy herds which had used controlled breeding programs (CBPs). This was compared with a database comprising a single calving season of 124 seasonally calving dairy herds using conventional artificial insemination programs (CAIs). Multiphase regression was used to derive two corrected databases by excluding outlying values of gestation length. The mean gestation length derived from the CBP database was slightly shorter than that of the CAI database (280.80 days, n = 775 versus 281.87 days, n = 1986; P < 0.001), but the two databases had similar variances (SD = 4.21 and 4.12 days, for CBP and CAI respectively; P = 0.36). The results from the multilevel analysis showed a mean difference in gestation length of 0.94 (SE 0.50) days; ( P = 0.06) between CAI and CBP. The mean gestation length and its SD from the CBP data were used to predict calving curves in herds using CBPs which could be compared with observed calving data. The observed and predicted calving patterns generated for two farms were not significantly different ( P = 0.37 and 0.31). The accuracy of the predictions was critically dependent on the completeness and accuracy of conception data and details for cows induced to calve prematurely.
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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 | 0.001 | 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) | 0.000 | 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".