Oceans and human health—navigating changes on Canada’s coasts
Bibliographic record
Abstract
Ocean conditions can affect human health in a variety of ways that are often overlooked and unappreciated. Oceans adjacent to Canada are affected by many anthropogenic stressors, with implications for human health and well-being. Climate change further escalates these pressures and can expose coastal populations to unique health hazards and distressing conditions. However, current research efforts, education or training curriculums, and policies in Canada critically lack explicit consideration of these ocean–public health linkages. The objective of this paper is to present multiple disciplinary perspectives from academics and health practitioners to inform the development of future directions for research, capacity development, and policy and practice at the interface of oceans and human health in Canada. We synthesize major ocean and human health linkages in Canada, and identify climate-sensitive drivers of change, drawing attention to unique considerations in Canada. To support effective, sustained, and equitable collaborations at the nexus of oceans and human health, we recommend the need for progress in three critical areas: ( i) holistic worldviews and perspectives, ( ii) capacity development, and ( iii) structural supports. Canada can play a key role in supporting the global community in addressing the health challenges of climate and ocean changes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".