The need for interdisciplinary solutions to climate change exemplified by harmful algal blooms
Bibliographic record
Abstract
It is generally understood that climate change is both a threat to health and a complex problem that requires accordingly complex solutions. In this commentary piece, I discuss the causes for and health implications of harmful algal blooms (HABs). I describe the effects that these blooms have on communities across Canada, especially in the Northern regions with particular focus on Indigenous communities who experience disproportionate harms due to HABs. I then examine Arctic Canada as a case study to motivate an interdisciplinary approach to understanding HABs which spans disciplines and knowledge systems. In doing this, I hope to illustrate the point that the causes and effects of HABs pose a problem too large to adequately address through any one field of study because of the complex and nebulous factors involved. Thus, the examination of this problem through alternative disciplines, ways of thinking, and world views, otherwise known as a “One Health”, collaborative, or trans-disciplinary approach, is warranted.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.043 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".