Mainstreaming Adaptation Climate Change into Strategic Environmental Assesment Case Study Banyuasin District, South Sumatra Province
Bibliographic record
Abstract
South Sumatra Province is one of the areas in Indonesia which tipped to be prone to the impact of climate change and very vulnerable due to its low-land areas that it may threat coastal, water, agriculture, and health sectors of the province. In Banyuasin District, the current program has been planned deep sea port development in ??Tanjung Api-api area. Coastal flood hazard components caused by a combination of sea level rise, storms, and La-Nina phenomena on maximum tide. In this study measured tidal hazards due to sea level rise. Risk area generated by layering hazard and vulnerability maps using GIS methods. The results of the risk assessment shows increase risk in the water availability at high risk level with percentage area ??38.87% and 46.02% at baseline conditions and projections. Adaptation efforts conducted to overcome the conditions mentioned above is by controlling the arrangement, the addition of retention ponds as a water storage and flood prevention efforts and also maintaining an area of green open space area above 30%. One of the priorities recommended program is a program of development and the development of sanitation where mitigation / adaptation recommended by increasing the efficiency of use of raw water, saving water resource utilization, and environmental sanitation. The results of this study need to be integrated / mainstreamed into development policies and plans regional / local so helpful to the development of a better way to identify the agenda on national plans, provincial and local levels are associated with adaptation to climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".