Evaluating micronutrient concentrations in liver samples from abortions, stillbirths, and neonatal and postnatal losses in beef calves
Bibliographic record
Abstract
The primary objective of the current study was to describe micronutrient concentrations in livers collected during postmortem examination from 501 beef calves and fetuses in western Canada. The most frequently identified deficiencies based on reported ranges for aborted fetuses were magnesium, copper, and vitamin E. For stillborn, neonatal, and postnatal calves, the most common deficiencies were magnesium, vitamin A, and vitamin E. However, for calves that died after 3 days of age, selenium deficiency was also very likely. Concentrations of all micronutrients examined in the study except zinc (P = 0.85) were different among fetuses, stillbirths, and neonatal and postnatal losses. This included liver selenium, copper, manganese, molybdenum, iron, magnesium, vitamin A, and vitamin E (P < 0.01). The percentiles of micronutrient concentrations for each age group, empirical prediction intervals for individual calves, and model predicted confidence intervals to compare with average concentrations from groups of calves were summarized for each age group. Information on cow, calf, and herd management attributes were available for 221 samples from 106 herds collected as part of study 1. Cow age, supplementation history, water quality, and herd location were associated with the concentration of iron, zinc, and molybdenum. The current study is unique in the number of samples included from young calves that were submitted as part of routine surveillance and not based on suspicion of either deficiency or toxicity. Most previous studies have been focused on older animals or fetuses and have contained a mix of beef and dairy animals with little or no information on their supplementation history.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".