Applying a One Health Lens to Mitigating Vehicular Impacts on Marine Mammals
Bibliographic record
Abstract
Marine vehicles play an important role in Newfoundland and Labrador’s (NL) economy, transportation, and essential services. However, they also pose significant threats to humans, marine mammals, and the environment via collisions, noise and/or light pollution, habitat destruction, emissions, and water contamination. These impacts are deeply interconnected, contributing to a complex and evolving wicked problem. Conservation efforts in NL, including grassroots initiatives and government regulations, reflect strong community interest but often lack enforcement mechanisms, long-term support, and measurable outcomes. Applying the One Health perspective highlights the value of community-informed, interdisciplinary approaches that address the complex and overlapping impacts of marine traffic, guiding the development of sustainable solutions for humans, marine mammals, and the environment.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".