Serum profiles of American Elk, Cervus elaphus, at time of handling for three capture methods
Bibliographic record
Abstract
Serum profiles of American Elk, Cervus elaphus, at the time of handling for three capture methods.Canadian Field-Naturalist 114(2): 196-200.American Elk (Cervus elaphus) are captured using a variety of techniques and each may inflict various types of stress and/or injury that could affect animal well-being.We compared serum profiles of 25 free-ranging elk captured by helicopter net-gunning (n = 7), modified Clover traps (n = 7), and corralling (n = 11) in the Black Hills, South Dakota.Glucose, aspartate aminotransterase, and lactic dehydrogenase levels were higher in Clover-trapped elk than corralled or net-gunned elk.Potassium and creatinine kinase levels were higher in elk captured by corralling than elk captured by netguns or Clover traps.Bilirubin was higher in Clover trapped and corralled elk compared to net-gunned elk.Our results suggest (1) techniques requiring less time from capture to release (i.e., net-gunning) significantly reduce tissue and muscle damage versus methods in which elk were confined for longer periods of time (i.e., Clover trapping and corralling), (2) limiting the time elk are restrained to < 24 hours in corrals may reduce muscle and tissue damage, (3) several serum parameters should be measured in order to obtain a complete description of elk response to capture and restraint.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".