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Record W4410589767 · doi:10.1038/s44183-025-00124-7

How to leverage trade to achieve a 2050 ocean dream

2025· letter· en· W4410589767 on OpenAlexafffund
U. Rashid Sumaila

Bibliographic record

Venuenpj Ocean Sustainability · 2025
Typeletter
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsFisheries and Oceans Canada
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLeverage (statistics)DreamBusinessEnvironmental scienceOceanographyComputer scienceGeologyArtificial intelligencePsychologyNeuroscience

Abstract

fetched live from OpenAlex

The Ocean is central to our lives, providing vital ecosystem goods and services. It generates 50% of the Earth's oxygen; absorbs around 30% of anthropogenic carbon emissions; regulates the Earth's climate; and provides food, income, and livelihoods for hundreds of millions of people worldwide. However, the Ocean is under serious multiple threats from overexploitation, climate change, and pollution. Here, I state my dream 2050 scenario for the Ocean and describe how trade, in the midst of broader ocean governance efforts, can contribute to realizing this dream.

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)0.006
metaresearch head score (Gemma)0.017
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.047
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0060.007
Scholarly communication0.0070.011
Open science0.0010.005
Research integrity0.0470.045
Insufficient payload (model declined to judge)0.0090.006

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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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
GenreCommentary

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 routes2
Has abstractyes

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