Effects of production system and growth promotants on the physiological maturity scores in steers
Bibliographic record
Abstract
López-Campos, Ó., Aalhus, J. L., Larsen, I. L., Juárez, M. and Basarab, J. A. 2014. Effects of production system and growth promotants on the physiological maturity scores in steers. Can. J. Anim. Sci. 94: 607–617. Over a 2-yr period, 224 crossbred steers were allotted to a 2×2×2 factorial arrangement of treatments to determine the effect of the production system (calf-fed vs. yearling-fed), growth implant strategy (non-implanted vs. implanted) and β-agonist supplementation (no ractopamine vs. ractopamine) on physiological indicators of maturity. Dentition and ossification scores along the vertebral column were collected post-mortem during head inspection and grading. Dentition score was significantly affected (P<0.001) by production system, but not by implant (P=0.68) or β-agonist (P=0.31). There were significant interactions (P<0.001) between production system and implant strategy on the frequencies of carcasses showing ossification in the thoracic, lumbar and sacral vertebral processes. There was a significant interaction (P<0.0001) between the production system and implant strategy on the frequencies of the carcasses considered as <21 or >21 mo of age based on a segregation model using only physiological maturity assessments. These data emphasize the inability of physiological scores to accurately reflect chronological age, with overall classification accuracies of 0.68 and 0.53 for dentition and ossification scores. The highest overall classification accuracies were obtained using the thoracic (0.74) or lumbar (0.69) ossification scores. Implants accelerate the ossification process, particularly in younger animals, thus having a dramatic effect on numbers of animals eligible to be categorized as <21 mo of age based on physiological maturity evaluation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".