Warning sign of an accelerating decline in critically endangered killer whales (Orcinus orca)
Bibliographic record
Abstract
Abstract Wildlife species and populations are being driven toward extinction by a combination of historic and emerging stressors (e.g., overexploitation, habitat loss, contaminants, climate change), suggesting that we are in the midst of the planet’s sixth mass extinction. The invisible loss of biodiversity before species have been identified and described in scientific literature has been termed, memorably, dark extinction. The critically endangered Southern Resident killer whale (Orcinus orca) population illustrates its contrast, which we term bright extinction; namely the noticeable and documented precipitous decline of a data-rich population toward extinction. Here we use a population viability analysis to test the sensitivity of this killer whale population to variability in age structure, survival rates, and prey-demography functional relationships. Preventing extinction is still possible but will require greater sacrifices on regional ocean use, urban development, and land use practices, than would have been the case had threats been mitigated even a decade earlier.
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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 | 0.000 |
| 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) | 0.004 | 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".