The climate change crisis: A call for action by Past Presidents of the American Fisheries Society
Bibliographic record
Abstract
ABSTRACT We, AFS Past Presidents, assert human- caused climate change must be combatted now if we wish to conserve the world’s aquatic ecosystems and fisheries. Failing to respond to efforts restricting climate change research, scientific knowledge sharing, and management strategies to alleviate human- caused climate change will have dire consequences for human societies and our planet. Here we briefly review the history of human-caused climate change, its mechanisms, and how American Fisheries Society members have researched and documented its effects on aquatic ecosystems over the years. We call upon everyone interested in functioning aquatic ecosystems to convey and build upon the strength of evidence supporting the understanding of human-caused climate change – despite climate change denials arising in public policy. We especially encourage reaching the broader public on the climate issue via popular publications, social media, presentations to user groups, displays, and both direct contact and encouragement of family, friends and other organizations to contact elected officials about the climate issue. Our duty to future generations requires us to use all available tools to influence policy in ways that will avert global crises.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.031 | 0.038 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".