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Record W7068880837

[no title]

2023· other· en· W7068880837 on OpenAlexfundno aff

Bibliographic record

VenueDirectory of Open access Books (OAPEN Foundation) · 2023
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
FundersDirektorat Riset dan Pengabdian MasyarakatNatural Sciences and Engineering Research Council of Canada
KeywordsPropulsionDiesel fuelThermal efficiencyFuel efficiencyEfficient energy useNitrogen oxidesCarbon oxideEnergy consumption
DOInot available

Abstract

fetched live from OpenAlex

Some marine propulsion systems are based on thermal machines that operate under the diesel cycle. Their main advantages, compared to other propulsion systems based on thermal machines, are low specific fuel consumption and greater thermal efficiency. However, their main disadvantages lie in the emissions produced by combustion, such as carbon dioxide (CO2), sulfur oxide (SOx), and nitrogen oxide (NOx). Over the last decade, the International Maritime Organization (IMO) has adopted a series of regulations to reduce these emissions based on the introduction of several energy efficiency designs and operational indicators. In this context, this book focuses on the design and operation efficiency of ships through an analysis of the main propulsion systems. It discusses the use of alternative fuels as well as the integration of hybrid and fully electric propulsion systems.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0070.006
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.050
GPT teacher head0.412
Teacher spread0.362 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueDirectory of Open access Books (OAPEN Foundation)Same topicMachine Learning in BioinformaticsFrench-language works237,207