CALCULATION OF OIL TANK VOLUME AND REPORT GENERATION SYSTEM WITH TRIM AND LIST CORRECTIONS
Bibliographic record
Abstract
The capacity of ship’s oil tanks is usually designed as a tabled form in order to obtain oil volumes by using the measured ullage heights. However, the tank walls easily deform or distort due to long-term heavy loading. This phenomenon may cause serious errors that the carrying capacity in oil tanker does not match with the values of the tabled form. In this paper, we perform an oil tank volume calibration project that aims to develop a tank volume calculation and report a generation software with trim and list corrections. The current internal specification for each tank is measured first, and then all specification data measured can be input to this software system to calculate each tank’s volume. These calculated results will be verified by actual delivery volume tests. This software system has been applied to the Der-Yun Oil Tanker of CPC Corp. The result shows that the overall error of calibrated volume for all tanks is under 0.1%. It is proved that this system highly improves the correctness of the vessel’s carrying capacity.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.013 | 0.005 |
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".