Quantitative Evaluation of Draught Survey Through Correlation Test of Quarter Mean: A Case Study on a Coal Bulk Carrier
Bibliographic record
Abstract
Complicated draught survey activities both at the data collection stage and the calculation stage, especially at sea, make it difficult for surveyors to accurately inform coal cargo volumes. However, in practice, most coal bulk-carrier surveyors can provide precise information on coal cargo volumes between ports up to a difference of less than 0.5%. This difference is not enough to be used as the only parameter in supervising draught survey activities. More effective monitoring needs to be done so that data reliability can be validated. This study aims to propose a new method of off-site surveillance of draught survey activities through correlation tests with a case study on a bulk-carrier ship less than 10 years old in all coal shipments at one of coal-fired power plant during year 2021. The results of the study show that based on the correlation test of Pearson (2-tailed), Spearman (2-tailed), and Kendall (2-tailed), during coal shipments in 2021 both at loading ports and at unloading ports, the interpretation of the quarter-mean as independent variable is at least strongly correlated with both displacement and displacement corrected for density, while the correlation of quarter mean with both net displacement and constant is not significant (negligible).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".