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Record W4367555119 · doi:10.3389/fmars.2023.1175270

Transitioning towards environmentally benign marine antifouling coatings

2023· article· en· W4367555119 on OpenAlexafffund
Andrew Carrier, Megan Carve, Jeff Shimeta, T.R. Walker, Xu Zhang, Ken D. Oakes, Kshitij C. Jha, Tim Charlton, Martina H. Stenzel

Bibliographic record

VenueFrontiers in Marine Science · 2023
Typearticle
Languageen
FieldEngineering
TopicMarine Biology and Environmental Chemistry
Canadian institutionsDalhousie UniversityCape Breton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationMitacsNational Research CouncilUniversity of New South WalesOcean Frontier InstituteCape Breton University
KeywordsBiofoulingIncentiveSustainable developmentMarine industryBusinessSustainabilityEnvironmental planningMarine ecosystemEnvironmental resource managementEcosystemNatural resource economicsEnvironmental scienceEcologyEconomicsChemistry

Abstract

fetched live from OpenAlex

Marine biofouling has been an issue since antiquity whose solutions have a history of negative environmental impact. The development of environmentally sustainable solutions is paramount as society is becoming more conscious of anthropogenic impacts on the global ecosystem, particularly the global oceans. Herein we include a brief overview of common strategies in the development of sustainable marine antifouling coatings in terms of their efficacy, durability, and environmental impact. We discuss technical challenges to the development of sustainable antifouling coatings; barriers and incentives to their market uptake; and advocate the necessity of multi-stakeholder collaboration, including scientists, engineers, industry groups, and regulators, toward the development of marketable and sustainable antifouling coating solutions.

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 score0.112
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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

Citations32
Published2023
Admission routes2
Has abstractyes

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