Associations between prebreeding serum micronutrient concentrations and pregnancy outcome in beef cows
Bibliographic record
Abstract
OBJECTIVE: To determine associations between serum concentrations of copper, molybdenum, selenium, vitamin A, and vitamin E measured in beef cows at the start of the community pasture breeding season and pregnancy status at the end of the season. DESIGN: Prospective cohort study. ANIMALS: 771 beef cows from 39 cow-calf herds. PROCEDURES: Serum micronutrient concentrations were measured in samples collected from cows on arrival at 5 different community pastures in Saskatchewan, Canada, in May 2008. Cows were palpated transrectally to determine pregnancy status in October 2008. Herd owners and professional herd managers were surveyed to collect individual data for cows (age, calving date, and history of exposure to bulls before the start of the breeding season) and information on herd and breeding management. Associations between animal-, herd-, and pasture-level variables and pregnancy status were examined. RESULTS: Serum concentrations of selenium, molybdenum, vitamin A, and vitamin E were not associated with pregnancy status after accounting for prebreeding body condition score, age, and calving-to-breeding interval. Serum copper concentrations were more commonly assessed as below adequate than were other micronutrients. Decreased serum copper concentrations were associated with increased odds of nonpregnancy in cows < 10 years of age. CONCLUSIONS AND CLINICAL RELEVANCE: Prebreeding micronutrient supplementation programs should be carefully managed in herds with poor reproductive performance in areas known to be copper deficient, and evaluation of serum copper concentrations from a subset of cows should be considered before the start of the breeding season.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.000 | 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".