The effects of bull exposure and lasalocid on the development of replacement beef heifers
Bibliographic record
Abstract
At weaning in the fall, crossbred heifers (n = 224), born in either the winter (January–February) or spring (March–April), were assigned on the basis of age, sire-breed and body weight to one of two similar winter housing facilities (with or without sterilized bulls), and to one of two forage-based (87%) diets (with or without lasalocid, 200 mg d−1) within each housing facility. Observations for estrus were made twice daily. Timed AI (66 h after PGF2α) was used to breed heifers for the first time at 14 mo of age. Plasma progesterone concentrations were used to confirm estrus/ovulation and to determine the PGF2α response rate. Bull exposure advanced puberty in winter-born heifers, but delayed puberty in spring-born heifers (P ≤ 0.029). Similarly, timed AI pregnancy for winter-born heifers was higher with than without bull exposure (58.9 vs. 32.5 ± 5.3%; P = 0.017) while the opposite occurred for the spring-born group (27.1 vs. 59.1 ± 4.7%; P < 0.001). Bull-exposed spring-born heifers were the oldest at calving, the latest to calve, and their calves had the slowest growth and lowest weaning weight means (P < 0.027). Lasalocid did not influence puberty (P ≥ 0.273), had a small effect on body weight gain (P ≥ 0.033) that did not limit attainment of optimal body weight or condition at AI, but enhanced response rate for spring-born heifers (P = 0.075) and conception rate for winter-born heifers (P = 0.047). The efficacy of bull exposure and lasalocid is dependent upon the proximity of heifers to the attainment of puberty when the treatments are introduced; further research is required to determine the most appropriate use of either management tool for developing beef replacement heifers. Key words: Puberty, heifer development, bull exposure, ionophore, estrus, conception
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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 | 0.000 |
| 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) | 0.001 | 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".