The plight of the enigmatic southern resident killer whales: Have we done all we can to recover these icons of the Salish Sea?
Bibliographic record
Abstract
Southern Resident killer whales recognize no boundaries but frequent the coastal waters of southern British Columbia (Canada) and northern Washington State (USA). Having acknowledged their conservation plight, the two respective national governments have afforded this distinct and much-valued population the status of ‘Endangered’ under their respective endangered species laws. Divergent natural resource management regimes, endangered species legislation, and marine use profiles in the two nations have at times limited a concerted conservation push for these killer whales. However, much has been learned over the past 20 years about the three primary threats to their recovery - diminished prey (primarily Chinook salmon), underwater noise, and high levels of industrial contaminants. This research has, in turn, led to a number of steps in the two jurisdictions to recover the SRKW and improve their habitat. This panel will review past successes and failures in the quest for killer whale recovery, and contribute to a forward-looking agenda that addresses a notable and timely opportunity: ‘What more can we do to recover SRKW?’. The panel will encourage attendees to reflect on constraints and opportunities on the path to recovery. The session will provide a safe place for ‘outside the box’ ideas where boldness and innovation are encouraged to address the challenges facing the species in this transboundary region.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".