MétaCan
Menu
Back to cohort
Record W4413472290 · doi:10.1007/s00267-025-02263-6

Planning Transitions: SEAs, Climate Change, and Energy Transitions in the Ocean

2025· article· en· W4413472290 on OpenAlexaff
Leah Fusco, Gerald G. Singh

Bibliographic record

VenueEnvironmental Management · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of VictoriaMemorial University of Newfoundland
FundersOcean Nexus Center, EarthLab, University of Washington
KeywordsClimate changeNature ConservationEnvironmental scienceForest managementEnergy transitionOceanographyClimatologyGeographyGeologyEcologyAgroforestryBiology

Abstract

fetched live from OpenAlex

Oceans have been gaining attention as a solution to climate change, including as sites of renewable energy development. Thus, oceans are becoming increasingly important sites of the energy transitions necessary for countries to meet climate change commitments. However, oil remains deeply entrenched in many places, often making it difficult to envision and enact different energy futures. This paper addresses the potential of strategic environmetal assessments (SEAs) to play a role in ocean-based energy transitions, specifically exploring whether they provide the information needed to support decisions related to energy transitions. By drawing on 69 offshore energy-related SEAs done in 11 countries between 2000 and 2021, this paper takes a broad view of ocean energy SEAs across time and space. In particular, we explore whether these SEAs consider climate change, include the broader context of climate goals, and assess alternatives. Findings varied by jurisdiction but in many cases, SEAs were done in ways that limited their potential to contribute to energy transition decisions. This is important because countries around the world have climate commitments and SEAs represent an opportunity - specifically one that uses existing and familiar tools - to help with long term planning around offshore energy that can help meet these commitments.

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 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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental ManagementSame topicEnvironmental and Social Impact AssessmentsFrench-language works237,207