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Record W4392705755 · doi:10.1038/s44183-024-00045-x

Key components of sustainable climate-smart ocean planning

2024· article· en· W4392705755 on OpenAlex
Catarina Frazão Santos, Tundi Agardy, Larry B. Crowder, Jon Day, Malin L. Pinsky, Amber Himes‐Cornell, Julie Reimer, Sara García-Morales, Nathan Bennett, Amanda T. Lombard, Helena Calado, Marinez Eymael García Scherer, Wesley Flannery, Lisa M. Wedding, Elena Gissi

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.

Bibliographic record

Venuenpj Ocean Sustainability · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsFundação para a Ciência e a TecnologiaH2020 Marie Skłodowska-Curie ActionsResearch Executive AgencyHORIZON EUROPE Framework ProgrammeUK Research and InnovationNational Research FoundationEuropean CommissionNational Science Foundation
KeywordsMarine spatial planningKey (lock)GlobeEnvironmental resource managementCorporate governanceEnvironmental planningBusinessClimate changeSustainable developmentGeographyEnvironmental scienceComputer scienceOceanographyPolitical science

Abstract

fetched live from OpenAlex

Abstract Planning of marine areas has spread widely over the past two decades to support sustainable ocean management and governance. However, to succeed in a changing ocean, marine spatial planning (MSP) must be ‘climate-smart’— integrating climate-related knowledge, being flexible to changing conditions, and supporting climate actions. While the need for climate-smart MSP has been globally recognized, at a practical level, marine managers and planners require further guidance on how to put it into action. Here, we suggest ten key components that, if well-integrated, would promote the development and implementation of sustainable, equitable, climate-smart MSP initiatives around the globe.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.004
Research integrity0.0000.000
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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