Analisis Dasar Perencanaan Pelabuhan di Rencana Ibu Kota Negara (IKN) Baru - Provinsi Kalimantan Timur
Bibliographic record
Abstract
In the transportation system, the port is a node in the chain of smooth sea and land transportation cargo, which then functions as a transition activity between modes of transportation. The importance of ports in a transportation system requires that each port have a basic framework for port development and construction plans. This basic framework is contained in a spatial development plan which is then described in stages of short, medium andl ong term development implementation. Port development studies are an integral activity in transportation planning in the development of the National Capital City (IKN). This research begins with an analysis of goods movement needs and location analysis. These two factors are the main factors determining port layout. Location analysis includes available land conditions, topographic conditions, hydrooceanographic conditions, including the available road network. It is known that the need for construction goods supplied from outside Kalimantan is 54,037,806 tons for 5 years. Thus, the annual average is 10.8 million tons. Assuming there are 260 working days in a year, then there is a flow of 41,569.2 tons per day. The existing port or terminal that will be developed into an IKN port is the PT Special Terminal. ITCI Hutani Manunggal (IHM) with planned activities for loading and unloading construction goods and the East Kalimantan Kariangau Port Terminal (KKT) loading and unloading civil logistics goods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".