Measurement of cortisol in blow samples collected from free‐swimming beluga whales (<scp><i>Delphinapterus leucas</i></scp>)
Bibliographic record
Abstract
Abstract Blow or respiratory vapor collection is a noninvasive technique for monitoring the physiology of cetaceans and although this technique shows promise, a major challenge of blow collection is difficulty quantifying samples due to variable amounts of seawater contamination. Here, we aimed to (1) determine whether blow samples could be collected from free‐swimming belugas, (2) assess whether urea could be used as a dilution marker to normalize blow samples, and (3) determine which factors influenced cortisol and urea concentrations. We collected a total of 252 blow samples from free‐swimming belugas in the Churchill River in Manitoba, Canada. Cortisol, an indicator of individual stress and health, was detected in 65.87% of samples with concentrations ranging from 6.73 to 963.17 pg/ml of extract volume, while urea was detected in 90.48% of samples with concentrations ranging from 0.21 to 20.30 mg/L. We were unable to verify the efficacy of urea as a dilution marker to normalize blow samples from free‐swimming belugas; however, absolute cortisol concentrations varied based on sample device and quantity rating. Although we demonstrated that blow can be successfully collected from free‐swimming belugas, further refinement of this technique is needed before it can be used as a reliable method for physiological assessments.
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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 | 0.001 | 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".