Comparison of trace mineral concentrations in tail hair, body hair, blood, and liver of mule deer ( <i>Odocoileus hemionus</i> ) in California
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.000 |
| 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 it