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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 OpenAlex
U. Rashid Sumaila

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
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 score0.351
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.009
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
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