Decision Support System for An Eco-Friendly Integrated Coastal Zone Management (ICZM) in Indonesia
Bibliographic record
Abstract
With the second longest coastline in the world (after Canada), Indonesia has a big challenge in managing its coastal zone. Ecologically, Indonesia’s coastal zone is rich with fascinating biodiversity; socioeconomically, it has played a long-time role as a sustainable source for food, as well as various development programs in Indonesia, such as interisland connectivity, shipping, fisheries, and logistics industries. The integrated coastal zone management (ICZM) concept is considered to be appropriate approach to deal with multi-stakeholders and multi-decision makers complexity in the coastal zone. In this paper, a decision support system (DSS) is developed based on ICZM by integrating numerical modelling and multi-parallel computing. This application system can be used as an interactive tool for managing the coastal area in Indonesia from various point of view, among other policymakers, industries, and coastal planners. The impacts after implementation of a scenario can be seen directly in the system to represent both the benefits and shortcomings. A test case is carried out in the Northern Jakarta coastal area. The system merits are highlighted in delivering direct effects after artificial islands instalment in the domain. DSS-ICZM development is intended to help policymakers in Indonesia improve the quality of their decisions and improve transparency for broad stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".