Learning Quality Management for Ships’ Upkeep and Repair Environment
Bibliographic record
Abstract
Ships Quality Management (QM) in a naval disciplined repair environment is under significant demands. The purpose of this paper is to emphasise the requirement for the establishment of quality management. Quality management is essential to support operations, work preparation and formulation, material replenishment, repairs and trial processes to enhance productivity and availability. Quality Assurance (QA) and Quality Control (QC) encompassing Reliability Centred Maintenance (RCM), Condition Based Maintenance (CBM) and Failure Modes, Effectiveness and Criticality Analysis (FMECA) are required to monitor Preventive and Corrective Maintenance with the aim of heading towards ISO. The QA and QC processes should be in line with a complete survey, Pre Upkeep Machinery Assessments (PUMA), Dynamic Machinery Trials (DMT) to predict and formulate the intended refurbishment followed by post upkeep standard trials. The whole process can be boosted when health and safety are integrated. The research instrument was based on the dedicated constructs to predict the ‘mindsets/opinions’ of employees in their perceptions of future improvement. The variables-hypotheses were inferentially analyzed (correlated and regressed). They were found to be positively related and significantly contributed to the ships’ upkeep support performance. The implication of this study is to understand the strength of the research framework and to make proposals for the enhancement of quality management in line with the variables.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".