The Identification of Typologies and Levels Utilization of Non-Commercial Ports in Indonesia Using the Machine Learning Method
Bibliographic record
Abstract
The Unitary State of the Republic of Indonesia (NKRI) is the most prominent nation with an archipelago.Indonesia has an area of 8,300,000 km 2 , with 16,056 islands registered.Indonesia's coastline reaches 108,000 km 2 , the second longest in the world.According to that, Indonesia needs well-developed and efficiently managed seaports.The number of seaports, according to the National Port Master Plan, is 636 Ports, of which the government operates 566 Ports as public ports (Non-Commercial Ports).The utilization of this port is shallow because an adequate market or hinterland does not support it.Non-commercial ports managed by the government must innovate to finance port operations and maintenance of port facilities.Innovations and breakthroughs can be made by improving port governance and optimizing the port service business.Improving port business processes to increase the optimization and utilization of ports requires port development strategies that must be formulated precisely.Port development strategies involve the identification of typologies and mapping the level of utilization of non-commercial ports based on typologies; This study uses machine learning methods to answer research objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".