Recursive systems model of fetal birth weight and calving difficulty in beef heifers
Bibliographic record
Abstract
The purpose of this study was to use readily available information, including dam pelvic width (PW) and height (PH) and the fetal coronet band (CB) measurement to predict the calving difficulty (CD) score of first-calf heifers under commercial ranch conditions. Data were collected from a cow-calf ranch over a 3-yr period. Using a recursive system of equations, two models were estimated. First, a linear model was used to predict birth weight (BTW) based on the fetal CB measurement. Second, an ordered logit model was used to predict calving difficulty score based on a nonlinear relationship with birth weight, pelvic dimensions of the dam, and interaction terms. The linear model demonstrated that BTW could be predicted using the CB measurement, both the intercept and slope coefficients were significant at P < 0.001. The model R2 was equal to 0.57 and the standard error of the predicted birth weight was 2.77 kg. The ordered logit model correctly predicted 468 of 684 (68.4%) of the CD scores. The results of this research suggest that it is possible to predict dystocia or calving difficulty on a case-by-case basis with information that is available to ranchers or ranch managers early in the parturition process. The management technique presented has been successfully adopted by some large-scale cow-calf operations, thus the results have commercial applications for beef producers. Key words: Dystocia, heifers, beef, recursive systems, ordered logit, coronet band, birth weight
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".