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Record W45633412 · doi:10.5957/jsp.2008.24.4.214

Major Factors Affecting Tugboat Ship Design and Construction

2008· article· en· W45633412 on OpenAlexaff
Biman Das, Navin Tejpal

Bibliographic record

VenueJournal of Ship Production · 2008
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsShipyardShipbuildingVendorReworkEngineeringNaval architectureManufacturing engineeringOperations researchOperations managementMarine engineeringBusiness

Abstract

fetched live from OpenAlex

Tugboat shipbuilding activities and costs were studied with a view to deal with the ship design and development problems and to suggest possible solutions. The lack of communication among shipyard design engineers, vendors, regulatory authority personnel, and shipyard construction department managers at the start of the preliminary and detail drawings gave rise to errors in material specifications in design drawings. This caused the shipyard a significant amount of rework on drawings, resulting in wasted labor costs and lengthening of the shipbuilding cycle time. The timely and correct vendor-furnished information (VFI) on material specification, while preparing the preliminary and detailed drawings, would permit the elimination/ reduction of errors and changes made in the drawings. A medium-sized tugboat ship costs about $14 million, comprising about $5 million labor cost and about $9 million of construction material cost. The completion time for this kind of vessel ranges from 28 to 38 months. It would be possible to reduce the labor and material costs and completion time substantially with adequate and timely information of material re- quirements from the vendor through VFI. Management tools and techniques, such as concurrent engineering, computer simulation, and program evaluation review technique (PERT) could be applied advantageously to improve tugboat shipbuilding de- sign and development productivity.

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 score0.301
Threshold uncertainty score0.418

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 studies0.0000.000
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 head0.023
GPT teacher head0.208
Teacher spread0.185 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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