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Record W4389053487 · doi:10.1038/s44183-023-00024-8

An international panel for ocean sustainability needs to proactively address challenges facing existing science–policy platforms

2023· article· en· W4389053487 on OpenAlexaff
Gerald G. Singh, Harriet Harden‐Davies, Wilf Swartz, Andrés M. Cisneros‐Montemayor, Yoshitaka Ota

Bibliographic record

Venuenpj Ocean Sustainability · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsDalhousie UniversitySimon Fraser UniversityUniversity of Victoria
FundersOcean Nexus Center, EarthLab, University of WashingtonEarthLab, University of WashingtonUniversity of Washington
KeywordsSustainabilityScience policySustainability scienceOcean scienceBusinessAccountabilityOcean observationsAppealEcosystem servicesEnvironmental resource managementClimate changeEnvironmental planningPolitical scienceSustainability organizationsEcosystemEnvironmental scienceOceanographyPublic administrationEcology

Abstract

fetched live from OpenAlex

Recent calls for an International Panel for Ocean Sustainability (IPOS) to provide consensus-based science advice for global ocean sustainability appeal to the successes of global science–policy platforms, specifically the Intergovernmental Panel on Climate Change (IPCC), the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services (IPBES), and the World Ocean Assessment (WOA)1. A new IPOS may facilitate global ocean sustainability, but only if it proactively addresses the challenges facing existing international science–policy platforms—namely representation, accountability, and politicization.

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)0.067
metaresearch head score (Gemma)0.059
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: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0070.005
Scholarly communication0.0160.012
Open science0.0040.016
Research integrity0.0280.036
Insufficient payload (model declined to judge)0.0260.010

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 head0.055
GPT teacher head0.336
Teacher spread0.281 · 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
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

Citations6
Published2023
Admission routes1
Has abstractyes

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