Threat assessment for northern bottlenose whales off eastern Canada
Bibliographic record
Abstract
A threat assessment was conducted for 15 threat categories to Northern Bottlenose Whales (NBW) at two nested geographic scales: for the endangered Scotian Shelf population specifically (SSDU), and for both populations of NBW (SSDU and Davis Strait-Baffin Bay Labrador Sea [DSBBLS] population) in the Northwest Atlantic (NWA). This included evaluating threats at both the individual and population level. The individual level of impact was assessed (for both the SSDU and NWA) as high or extreme for the threats of historical whaling, military sonar, entanglement, risks of depredation, vessel strike, and oil spills. The population level of impact for the SSDU was assessed as either high or extreme for climate change, historical whaling, military sonar, entanglement, vessel strikes, and oil spills. The population level of impact for the NWA was assessed as high for historical whaling, climate change was assessed as medium, and vessel noise was assessed as low, while the other 12 threats were assessed as unknown, primarily because there is no information on the size of the DSBBLS population of NBW. Categorization of a particular threat as unknown at the individual or population level of impact does not indicate a lack of effect or that the threat is not important. In many cases impacts are known to occur on individuals even if population level impacts have not been or cannot easily be measured. It is likely that mortalities, injuries, and other impacts are underreported due to the offshore habitat of NBW. This threat assessment does not take into account impacts on habitat, indirect effects, interactions between multiple threats, or cumulative effects. The impacts of multiple threats combined may result in higher overall threat risk for NBW than any individual threat on its own. Climate change is a particularly concerning threat which may alter the level of risk of other threats to NBW.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".