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Record W4399317405

Trace mineral concentrations in Canadian beef calves at weaning.

2024· article· en· W4399317405 on OpenAlexaffabout
Cheryl Waldner, John Campbell

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTrace mineralBeef cattleTrace MineralsAnimal scienceTRACE (psycholinguistics)WeaningMineralEnvironmental scienceBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Objective: To describe the copper and selenium statuses of beef calves at weaning. Animal: = 1998) were sampled from 106 Canadian cow-calf herds in the fall of 2021. Procedure: Serum samples from calves were tested for copper, selenium, and molybdenum concentrations. Results: Although the percentages of calves classified as selenium deficient (< 0.025 ppm) were relatively low (0.5% western Canada, 3% eastern Canada), 53% of calves from western Canada and 77% of calves from eastern Canada were classified as having less than adequate selenium concentrations (< 0.08 ppm). Copper deficiency (< 0.5 ppm) was common in calves from both western (17%) and eastern (14%) Canada. High molybdenum concentrations (> 0.10 ppm) were identified in 6% of calves from western Canada and 7% of calves from eastern Canada. Conclusion: < 0.001). Copper and molybdenum concentrations were not significantly different between western and eastern Canada. Less-than-adequate serum copper was the most common deficiency identified in Canadian beef calves at weaning. Clinical relevance: Trace minerals are important for immune system function in calves at weaning. Selenium concentrations in calves at weaning were lower than in cows from the same herds collected at pregnancy testing 2 y earlier. Copper deficiency was also identified, though less frequently than for mature cows. Supplementation programs for calves should be customized based on testing and recognize both regional and age differences in risk.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.243
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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