Dive, food, and exercise effects on blood microparticles in Steller sea lions (<i>Eumetopias jubatus</i>): exploring a biomarker for decompression sickness
Bibliographic record
Abstract
Recent studies of stranded marine mammals indicate that exposure to underwater military sonar may induce pathophysiological responses consistent with decompression sickness (DCS). However, DCS has been difficult to diagnose in marine mammals. We investigated whether blood microparticles (MPs, measured as number/μl plasma), which increase in response to decompression stress in terrestrial mammals, are a suitable biomarker for DCS in marine mammals. We obtained blood samples from trained Steller sea lions (Eumetopias jubatus, 4 adult females) wearing time-depth recorders that dove to predetermined depths (either 5 or 50 meters). We hypothesized that MPs would be positively related to decompression stress (depth and duration underwater). We also tested the effect of feeding and exercise in isolation on MPs using the same blood sampling protocol. We found that feeding and exercise had no effect on blood MP levels, but that diving caused MPs to increase. However, blood MP levels did not correlate with diving depth, relative time underwater, and presumed decompression stress, possibly indicating acclimation following repeated exposure to depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".