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Record W4412794607 · doi:10.1126/science.adq0174

Governing novel climate interventions in rapidly changing oceans

2025· review· en· W4412794607 on OpenAlexaff
Tiffany H. Morrison, GT Pecl, Kirsty L. Nash, Terry P. Hughes, Philippa J. Cohen, Cayne Layton, Katrina Brown, Catherine E. Lovelock, Maria Carmen Lemos, W. Neil Adger, Sarah Lawless, Georgina G. Gurney, Elizabeth Mcleod, Katherine E. Mills, Imani Fairweather‐Morrison, Michael Phillips, Andrew Sullivan, Nathalie Hilmi, Lucy Holmes McHugh, Sisir Kanta Pradhan, Robert P. Streit, Navam Niles, Emily Ogier

Bibliographic record

VenueScience · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychological interventionUnintended consequencesPaceCorporate governanceLegitimacyClimate changeIntervention (counseling)BusinessEnvironmental resource managementEnvironmental planningNatural resource economicsPolitical scienceEnvironmental scienceEconomicsEcologyGeographyPolitics

Abstract

fetched live from OpenAlex

Marine systems are rapidly changing in response to global heating. The scale and intensity of change are triggering a host of novel interventions to sustain oceans and ocean-dependent societies. However, the pace of new interventions is outstripping capacity to prevent unintended consequences because governance systems to ensure responsible transformation of marine systems are not yet in place. Responsible transformation entails transitioning marine systems to sustainable, equitable, and adaptive states through weighing intervention risks against benefits, resolving ethical liabilities, improving social cobenefits, establishing legitimacy, and managing climate policy integrity. Global, national, and local actors must urgently convert responsible transformation principles into rules-and practice-to ensure that novel marine-climate interventions are safe, equitable, and effective.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.036
GPT teacher head0.320
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations12
Published2025
Admission routes1
Has abstractyes

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