The contribution of cargo loading and discharging time to the loss and gain of coal: Empirical evidence from Indonesian ports
Bibliographic record
Abstract
This research aimed to know both the direct and indirect contribution of cargo loading and discharging time to the Loss and gain of coal mediated by load quantity. The process of data collecting was done through secondary data taken from the loading port of Jetty in Samarinda Port and the discharging port of Muara Berau, East Kalimantan. During 2020, there was an average loss of coal cargo of as much as 56 percent. This was caused by the long waiting discharge time during the loading and discharging. Another problem was the long waiting discharge time, as many Mother Vessels, tugboats, and barges entered the Jetty port and made a density there. The research method used path analysis with the loading-discharging unit as the source of secondary data on the determining factors of load quantity and the Loss and gain of coal cargo. This research indicated that one of the dominant factors was waiting discharge time, as a problem frequently occurred when vessels would berth in the port for discharging activities. The key finding was the necessity for the competent party to pay special attention to the factors contributing to vessels’ waiting discharge time in the port by providing services as maximally as possible through human resources improvement in the form of training.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".