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Record W4307552289 · doi:10.3390/en15217910

Ship Energy Efficiency and Maritime Sector Initiatives to Reduce Carbon Emissions

2022· article· en· W4307552289 on OpenAlex

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

VenueEnergies · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsEnforcementSustainabilityRenewable energyMaturity (psychological)Environmental economicsGreenhouse gasBusinessClean technologyEfficient energy useScale (ratio)Natural resource economicsEngineeringEconomics

Abstract

fetched live from OpenAlex

With stricter IMO regulations on CO2 taking effect in 2023 and ambitious goals to reduce carbon intensity by 2030, the maritime industry is scrambling to clean up its act. Conventional methods and equipment are now being reevaluated, upgraded or completely replaced. The difference between a short-term fix and a long-term sustainable option is how flexible vessels will be to use new energy sources or technology as they become viable. The review discusses the recent literature on renewable energy sources, technical and operational strategies for new and existing ships, technology maturity, and alternative fuels. It is found that the IMO’s targets can be met by combining two or three technologies, or via a radical technology shift which can provide innovative, high-efficiency solutions from an environmental and economic standpoint. It has also been noted that policies and enforcement are essential management instruments for mitigating the unfavourable environmental effects of marine transportation and directing the maritime industry toward sustainability on a regional, national, and international scale.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.991

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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