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Record W7023427811

Open Hatch Carriers - Future Vessel Designs & Operations

2017· article· en· W7023427811 on OpenAlexaboutno aff

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2017
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsNewsprintFuel efficiencyContainer (type theory)Greenhouse gasRange (aeronautics)Unit (ring theory)
DOInot available

Abstract

fetched live from OpenAlex

The development of Open hatch carriers (OHC) dates back to the early 1960s linked to transport of newsprint from the paper mills along the coast of British Columbia (Canada) to the news-printers in San Francisco and Los Angles (USA). Prior to that, conventional general cargo ships, tween-deck liners and trampers transported newsprint and lumber (timber). The present OHC fleet transport a wide range of commodities in addition to the initial newsprint, i.e. timber (lumber), fertilizer (both as bulk and in bags), minor bulk, containers, project cargoes and even road units on multi decks. This implies that the present OHC fleet are competing with dry bulkers for typical dry bulk cargoes, and with container vessels and Ro-Ro's for cargo types, which requires more careful handling. The paper presents an overview of the historic development of transport efficiencies from the steam ships used in newsprint and timber trades in the early 1900 up to the latest generation of OHC's. Followed by a parametric feasibility study focusing on identifying cost and improvement potentials for new alternative designs versus the present. The results indicates that alternative combinations of main measurements to enable lower block coefficients reduces fuel consumption and greenhouse gas emissions (GHG) per freight unit transported. Moreover, these designs might increase the competitiveness of Open Hatch vessels versus their competitors, i.e. dry-bulk, container and Ro-Ro.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score0.924

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 communication0.0000.001
Open science0.0020.001
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 head0.069
GPT teacher head0.306
Teacher spread0.237 · 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 designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

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