Assessing the efficacy of ecological reserves: killer whale beach rubbing behaviour and vessel disturbance
Bibliographic record
Abstract
Area-based protection is an important tool for safeguarding key habitat. Reserves that focus on mitigation of specific threats are particularly effective and are more likely to support a measurable outcome. In the marine environment, reserves that limit vessel presence have the potential to reduce disturbance to marine mammals. However, assessing the efficacy of reserves has been an ongoing challenge. Physical and acoustic disturbance from vessels is recognized as a primary threat to recovery for the northern resident killer whale (NRKW) population in Canadian Pacific waters. The Robson Bight Michael Bigg Ecological Reserve (RBMBER) was developed to support the behaviour of beach rubbing, a culturally distinct and traditionally important activity. Beach rubbing provides a rare opportunity to quantify vessel disturbance of a behaviour associated with a fixed geographic location, identifiable by visual cues, and verifiable acoustically. Observations on vessel presence, NRKW rubbing frequency, and duration were collected from a beach inside the reserve and compared to a beach in proximity to, but outside of, the RBMBER. In 2019-2022, vessel counts near the RBMBER beach were significantly lower than near the unprotected beach, and overall, rubbing occurred more frequently inside the reserve (78% of visits) than outside (35%). However, outside the reserve, concurrent vessel presence did not predict the occurrence of rubbing activity, indicating that vessel presence may negatively affect beach rubbing through long-term learned avoidance of frequently impacted areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".