Assessing dual hair sampling for isotopic studies of grizzly bears
Bibliographic record
Abstract
Rationale The stable isotope ratios of carbon ( δ 13 C values), nitrogen ( δ 15 N values) and sulfur ( δ 34 S values) in bear hair can be used to obtain information on dietary history. Sample protocols often require hair sampling from multiple anatomical locations; however, there remains a question as to whether this is necessary for isotopic studies of hair. The purpose of this study was to determine whether significant differences can be observed for the δ 13 C, δ 15 N and δ 34 S values between paired hair samples taken from the rump and shoulder of grizzly bears ( Ursus arctos ). Methods Paired hair samples were collected from the rump and the shoulder of 81 grizzly bears in the Yukon, Canada. Hair samples were analyzed using a thermal combustion elemental analyzer coupled with a continuous flow isotope ratio mass spectrometer. Results Statistical comparisons of paired hair samples for both males and females showed no meaningful differences in δ 13 C, δ 15 N and δ 34 S values in hair taken from the rump and shoulder, and any observed differences fell within the instrumental error. Conclusions Based on these results, hair may be safely sampled on either the rump or the shoulder without loss of isotopic information and thus this finding allows for refinement of sampling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".