Fireproof killer whales (<i>Orcinus orca</i>): flame-retardant chemicals and the conservation imperative in the charismatic icon of British Columbia, Canada
Bibliographic record
Abstract
Long-lived and high trophic level marine mammals are vulnerable to accumulating often very high concentrations of persistent chemicals, including pesticides, industrial by-products, and flame retardants. In the case of killer whales (Orcinus orca), some of the older individuals currently frequenting the coastal waters of British Columbia (BC) were born during the First World War, well before the advent of widespread chemical manufacture and use. BC's killer whales are now among the most polychlorinated biphenyl (PCB) contaminated marine mammals in the world. While the "legacy" PCBs have largely been banned, polybrominated diphenyl ethers (PBDEs) have recently emerged as a major concern. The endocrine-disrupting nature of these two persistent fire retardants in biota spells trouble at the top of the food chain, with increasing evidence of effects on reproductive health, the immune system, and development in exposed mammals. The heavy contamination of BC's killer whales, coupled with their long life span and high trophic level, highlights the need for a "weight of evidence" approach in research, conservation planning, and regulatory decisions. Given the global nature of contaminant dispersion, such approaches can only be effective when carried out on both national and international scales.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".