Comparison of trace mineral concentrations in tail hair, body hair, blood, and liver of mule deer ( <i>Odocoileus hemionus</i> ) in California
Bibliographic record
Abstract
Measuring trace mineral concentrations can be an important component of assessing the health of free-ranging deer. Trace mineral concentrations in liver most accurately reflect the trace mineral status of an individual, but, in live animals, whole blood or serum are the most commonly used sample types. Trace minerals measured in serum, such as copper, zinc, and iron, do not always accurately correlate to liver concentrations, and supplementary samples for evaluating the trace mineral status in live deer would be useful. We evaluated the utility of body and tail hair for measuring selenium, copper, zinc, iron, and manganese in free-ranging mule deer (Odocoileus hemionus) by using Spearman rank correlations and linear regression. Correlations were strongest at the time of or shortly after growth of the winter coat and in resident deer. In live deer, strong correlations and moderate linear associations (R (2) = 0.57) were detected between body and tail hair and whole blood selenium in December. In postmortem-sampled deer, a strong correlation and linear association (R (2) = 0.80) were found between liver and body hair selenium in August-November. Results indicate that body hair, if collected during or shortly after growth of the winter coat, can be used as a supplementary sample for measuring selenium concentrations in deer. None of the other correlations and linear associations were found to be sufficiently strong to conclude that hair can reliably be utilized as a complementary sample for measuring these trace mineral concentrations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".