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Managing marine resources sustainably – But how do we know when marine management has been successful?

2025· article· en· W4408728894 on OpenAlexaff
Michael Elliott, Ángel Borja, Roland Cormier

Bibliographic record

VenueOcean & Coastal Management · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsFisheries and Oceans Canada
FundersHORIZON EUROPE European Research CouncilEuropean CommissionUK Research and Innovation
KeywordsSustainabilityBusinessMarine conservationEnvironmental resource managementMarine protected areaEnvironmental planningFisheryGeographyEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Current marine environmental management is the ‘sustainable management of people and their marine activities’ to be achieved through ecosystem manipulation and activity control. Marine management per se needs to define who requires and can achieve a successfully managed environment, the tools and indicators for that management, the indications of success and the means of knowing that the environment has been successfully managed. Indicators of success require policies and plans, environmental targets and regulatory standards and guidelines, i.e. output controls such as those stipulated by legislation. It should aim for sustainable outcomes based on government policy, to satisfy public demands and using the advice and assessments by natural and social scientists. The inputs, outputs and outcomes should include scientific research and advice, reporting to the government and the public as well as compliance in programme performance evaluations. In this, there are three interested bodies: (i) those requiring a successfully managed environment such as the public; (ii) those responsible to carry-out and monitor the programmes and regulate humans and their activities as mandated by government such as administrators and regulators, and (iii) those implementing the management measures. Here, examples from Europe and North America but with relevance to all maritime states are used to emphasise that management success encompasses a well-defined planning cycle with a vision achieved as the result of objectives being met leading to actions carried out leading to outputs produced leading to outcomes achieved.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.835
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.048
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations16
Published2025
Admission routes1
Has abstractyes

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