Evaluating the use of hair as a non-invasive indicator of trace mineral status in woodland caribou (Rangifer tarandus caribou)
Bibliographic record
Abstract
Trace mineral imbalances can have significant effects on animal health, reproductive success, and survival. Monitoring their status in wildlife populations is, therefore, important for management and conservation. Typically, livers and kidneys are sampled to measure mineral status, but biopsies and lethal-sampling are not always possible, particularly for Species at Risk. We aimed to: 1) determine baseline mineral levels in Northern Mountain caribou (Rangifer tarandus caribou; Gmelin, 1788) in northwestern British Columbia, Canada, and 2) determine if hair can be used as an effective indicator of caribou mineral status by evaluating associations between hair and organ mineral concentrations. Hair, liver, and kidney samples from adult male caribou (nHair = 31; nLiver, nKidney = 43) were collected by guide-outfitters in 2016-2018 hunting seasons. Trace minerals and heavy metals were quantified using inductively-coupled plasma mass spectrometry, and organ and hair concentrations of same individuals were compared. Some organ mineral concentrations differed from other caribou populations, though no clinical deficiency or toxicity symptoms were reported in our population. Significant correlations were found between liver and hair selenium (rho = 0.66, p<0.05), kidney and hair cobalt (rho = 0.51, p<0.05), and liver and hair molybdenum (rho = 0.37, p<0.10). These findings suggest that hair trace mineral assessment may be used as a non-invasive and easily-accessible way to monitor caribou selenium, cobalt, and molybdenum status, and may be a valuable tool to help assess overall caribou health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".