Tempering impacts and embracing curiosity: ethical practices and technological considerations for the marine wildlife ecotourism industry
Bibliographic record
Abstract
Seabird and marine mammal ecotourism can provide robust economic benefits for coastal communities near seabird colonies and biodiversity hotspots. Ecotourism also promotes increased public knowledge of vulnerable species and conservation issues, and as a result, garners increased public support for environmental protection. However, ecotourism can also cause conservation concerns. For example, increased ecotourism presence (i.e., boats and tourists near seabird colonies) can result in increased disturbance and decreased reproductive success and localized population declines in some species. It is, therefore, critical to find a balance between promoting the enjoyment and economic prosperity associated with marine ecotourism and tempering impacts incurred by species of interest. Technological and research advancements can be used to make ecotourism more accessible to the public and less damaging to the ecosystem. We recommend and discuss ethical best practices that marine ecotourism industries should consider, such as further study of buffer distances from sensitive locations and species. We also discuss technological improvements that can facilitate ecotourism and minimize harm, including replacing fossil-fuel powered boats with electric motors and the use of high-definition cameras aimed at seabird colony sites in place of close approach by tourists.
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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 | 0.063 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".