A System Dynamic Model for Sustainable Water Resource Management in Wangi-Wangi Island, Wakatobi, Indonesia
Bibliographic record
Abstract
The stewardship of water resources, a vital pillar of any ecosystem, underpins the evolution of sustainable socio-economic systems, particularly on islands.This study employs a dynamic system analysis to model sustainable water resource management on Wangi-Wangi Island in Wakatobi Regency, an area prone to raw water deficits.Four distinct scenarios have been constructed for this analysis: the existing 'Business as Usual' scenario (BAU), Scenario-1 (water conservation efforts and water-saving movements increased by 5%), Scenario-2 (an extension of Scenario-1 with a 7.5% increase in interventions and a 2.5% tourist growth rate control), and Scenario-3 (an expansion of Scenario-2 with an additional 5% increase in interventions).Preliminary system dynamic analysis outcomes indicate that, under the BAU scenario, the Wangi-Wangi Islands could face a raw water shortfall of -1,293,622 m 3 /year from 2016 through 2055.This underscores an urgent need for policy intervention to foster a sustainable water resource management system.Under Scenario-1, a modest improvement is predicted, with a small surplus of water availability (543,785 m 3 /year) projected.Scenario-2 anticipates a surplus of 1,690,506 m 3 /year until the period of 2044-2047, while Scenario-3 predicts a surplus of 1,085,029 m 3 /year through the end of the projection period.These projected surpluses, however, are contingent upon robust support from all stakeholders in terms of policy enforcement, financial investment, and public awareness.This study's findings underscore the critical need for comprehensive, sustainable water resource management policies and practices in island settings to avert impending water crises.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".