Cancer in wildlife, a case study: beluga from the St. Lawrence estuary, Québec, Canada.
Bibliographic record
Abstract
A population of approximately 650 beluga (Delphinapterus leucas) inhabits a short segment of the St. Lawrence estuary (SLE). Over 17 years (1983-1999), we have examined 129 (or 49%) of 263 SLE beluga carcasses reported stranded. The major primary causes of death were respiratory and gastrointestinal infections with metazoan parasites (22%), cancer (18%), and bacterial, viral, and protozoan infections (17%). We observed cancer in 27% of examined adult animals found dead, a percentage similar to that found in humans. The estimated annual rate (AR) of all cancer types (163/100,000 animals) is much higher than that reported for any other population of cetacean and is similar to that of humans and to that of hospitalized cats and cattle. The AR of cancer of the proximal intestine, a minimum figure of 63 per 100,000 animals, is much higher than that observed in domestic animals and humans, except in sheep in certain parts of the world, where environmental contaminants are believed to be involved in the etiology of this condition. SLE beluga and their environment are contaminated by polycyclic aromatic hydrocarbons (PAHs) produced by the local aluminum smelters. The human population living in proximity of the SLE beluga habitat is affected by rates of cancer higher than those found in people in the rest of Québec and Canada, and some of these cancers have been epidemiologically related to PAHs. Considered with the above observations, the exposure of SLE beluga to PAHs and their contamination by these compounds are consistent with the hypothesis that PAHs are involved in the etiology of cancer in these animals.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".