Mini-Review: The importance of an integrated approach to assess trace mineral feeding practices in dairy cows
Bibliographic record
Abstract
This mini-review was undertaken to demonstrate the impact of trace mineral feeding management of dairy cows on the ecosystem by discussing their role in the animal metabolism, dietary recommendations, current feeding practices, and their excretion in manure pertaining to five trace minerals, i.e., cobalt, copper, iron, manganese, and zinc. The first part of this mini-review relates the importance of trace minerals in dairy cow metabolism, and how recommendations are obtained. The following section showed that the transition period from the dry to the lactating phase is challenging for dairy cattle, and current trace mineral recommendations have been questioned for this period due to the role of some trace minerals in immunity and oxidative metabolism. Furthermore, trace mineral overfeeding is a common practice in intensive dairy production system in Canada, the USA, and the UK, which is far from precision nutrition. Trace minerals in excess of requirements are directly excreted into the manure. The practice of trace mineral overfeeding could have detrimental effects on the ecosystem when manure with high trace mineral concentrations is repeatedly spread on fields. In conclusion, an integrative approach assessing the impact of trace mineral overfeeding in cow diets on the ecosystem is needed.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".