Kajian Teknologi Sand by Passing Penanggulangan Sedimentasi dan Erosi Pantai Bengkulu (Pelabuhan Pulau Baai)
Bibliographic record
Abstract
Curently the port flow conditions Baai Island can no longer be passed if the large size of the ships that will stop at the port. This is because the rut depth at this point is just -2m until -4m LWS, from a normal condition that should be -10m until -12m LWS.This situation is certainly very disturbing process of exit and entry of goods and service to the province of Bengkulu throught this port, and negatively impact the local economy.The aim of this thinking is to provide input for the achievement of an optimal solution to overcome sedimentation arround Baai island port Bengkulu to know the behaviourof the sedimentation ponds arround the harbour entrance and the effect on navigation channel.The scope of research is supporting data collection relating to the port Baai Island Bengkulu including development planning reports, Baai harbour and reports on the sedimentation and the condition of the harbour. The method of analysis used in this study were laboratory analysis techniques. Analysis of what has been studied to mentionthat the large amount of sedimen transport (litoral transport) along the coast of the port based on wave direction,among others from the west and south-west and north, are as follows: 1) total sediment transport (Qs) which took place on the beach ports Baai island is: 601,576.20m3/year. 2)The sediment transport that provides the greatest contribution to the sedimentation flow Baai harbour island is the result of calculation is from the west (toward the most dominant),namely: Qs-net = 573,916.72 m3/year.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".