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Record W4410588337 · doi:10.1038/s44183-025-00129-2

The opportunity for climate action through climate-smart Marine Spatial Planning

2025· review· en· W4410588337 on OpenAlex
Ana M. Queirós, Talya ten Brink, María Bas, Christopher J. Sweeting, Sean McGuinness, Elizabeth Talbot, Patrick Berg Sørdahl, Christian Lønborg, Shayna R Deecker-Simon, Michael Elliott, Rafael Sardá, Jose A Fernades-Salvador, Christina Pretty, Riku Varjopuro, Elina Virtanen, Elena Gissi, Katherine L. Yates, Andrea Morf, Catarina Frazão Santos, Inne Withouck, Matthew Frost, Marta Coll, Kira Gee, Caitríona Nic Aonghusa

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 · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsFisheries and Oceans Canada
FundersHORIZON EUROPE European Research CouncilNatural Environment Research CouncilHORIZON EUROPE Framework ProgrammeUK Research and InnovationHorizon 2020 Framework ProgrammeAgència de Gestió d'Ajuts Universitaris i de RecercaGeneralitat de CatalunyaHavs- och Vattenmyndigheten
KeywordsMarine spatial planningAction (physics)Climate changeEnvironmental resource managementSpatial planningEnvironmental planningEnvironmental scienceGeographyOceanographyGeology

Abstract

fetched live from OpenAlex

Despite global climate-driven change in marine ecosystems and associated economic sectors, climate-smart Marine Spatial Planning (CSMSP) implementation remains limited. This joint perspective from across the climate research and Marine Spatial Planning policy interface discusses reasons for CSMSP's slow pace (blockers) and shares operational examples about how CSMSP is working around the world (enablers). Learning from national CSMSP contexts can help deliver needed and faster international collaboration on climate action.

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)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.009
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.351
Teacher spread0.308 · 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