Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".