Using Long-Term Ecological Research to Promote Sustainable Whale-Watching Practices in Clayoquot Sound, British Columbia
Bibliographic record
Abstract
Whale watching has experienced rapid growth worldwide while management of the industry has typically lagged behind, assumed an apparent precautionary approach, and lacked an ecological understanding of the species of focus. Considering both socioeconomic and ecological factors in tandem and not as isolated circumstances is important when managing wildlife and related tourism activities, including whale watching. The goal of this article is to address the research gap between social and ecological components in wildlife tourism management using a case study from the University of Victoria Whale Research Lab that has been collecting ecological data surrounding gray whale presence in Clayoquot Sound, Canada for almost 30 years. Results indicate that the boat behavior with respect to whales as well as whale-watching industry pressure depend on the ecological factors that contribute to whale presence. Based on this information, I propose five management recommendations that promote sustainable development and use of the commercial whale-watching industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 teacher head, 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".