Ship performance monitoring and analysis to improve fuel efficiency
Bibliographic record
Abstract
A pilot project was launched to monitor vessel performance and to explore ways to reduce fuel consumption. A prototype Vessel Performance Monitoring and Analysis System (VPMAS) was used to collect information over a three week period. The project objective was to collect needed data, conduct preliminary analysis to establish trends, explore key performance indicators (KPI) to establish baseline, and explore data products for performance management. Performance management includes improving vessel performance and supporting efficient operation to reduce fuel consumption. For the pilot project, only a subset of vessel performance data was collected. Current key performance indicators (KPIs) include fuel consumption per trip, fuel consumption per distance travelled, fuel consumption per displacement distance and fuel consumption per payload distance. The dataset will expand in the future and will include the effect of environmental conditions. Preliminary analysis includes comparing the normal route for calm sea states and irregular routes taken probably to avoid heavy sea states; assessing the maneuvers in and out of harbors, computing key performance indicators, assessing the data trends and general statistics, and identifying data products to support performance management. Initial results show that automatic fuel measurement was in good agreement with manual tank sounding. A voyage on an irregular route consumed almost twice the amount of fuel consumed in a normal route. Fuel consumption would be reduced if constant speed is used in open water and if deviations from the desired routes could be minimized, for example, through optimized autopilot.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".