Penilaian Kerentanan Pesisir Berdasarkan Parameter Fisik di Pantai Utara Kabupaten Bekasi
Bibliographic record
Abstract
Northern part of Bekasi Regency (Pantura Bekasi) has diverse land: a port area, fisheries, marine tourism, agriculture, residential, industrial and government. Various types of development are occured to support the community welfare. Developments occur without considering sustainability will result in a decline of environmental conditions, and each region will have its own ability to anticipate the impacts of changes that occurs. This research studied coastal vulnerability in the Pantura Bekasi and the relationship between their coastal physical parameters and the vulnerability. Field observations were also conducted as field validation and perceive current conditions. The results show that vulnerability is very high in Fishery port (PPI) Muara Jaya port zone (Mekar Coast), and low vulnerability occurred in Taruma Jaya port zone (Taruma Jaya Coast). Two paramaters are different in these two locations, they are coastline change and geomorphological. The coastline change at Mekar Coast is abrasion, and Taruma Jaya Coast is accretion. The geomorphological at Mekar Coast are a muddy beach and delta, while Taruma Jaya Coast is a swampy beach. Field conditions show that Mekar Coast has low mangrove density, while Taruma Jaya Coast has high mangrove density. These results hopefully can be used as policy consideration for the local government in optimizing coastal management planning, where the spatial plan for Pantai Mekar Beach is designated as a conservation area and demersal fisheries, while on Pantai Taruma Jaya as a public use area such as for Gas and Steam Power Plant (PLTGU) Muara Tawar, PPI Paljaya and port zone.
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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 | 0.001 | 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".