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Record W4413418647 · doi:10.1093/fshmag/vuaf074

The climate change crisis: A call for action by Past Presidents of the American Fisheries Society

2025· article· en· W4413418647 on OpenAlexaff
Scott A. Bonar, Charles C. Coutant, Ron Essig, Mary C. Fabrizio, William L. Fisher, Robert M. Hughes, Wayne A. Hubert, Christopher C. Kohler, Steve L. McMullin, Christine M. Moffitt, Brian R. Murphy, Henry A. Regier

Bibliographic record

VenueFisheries · 2025
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of TorontoMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsCall to actionAction (physics)Climate changeFisheryPolitical scienceBusinessEcologyBiologyMarketing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.299
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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