Assessment of Food Supply Ecosystem Services and Water Supply Ecosystem Services to Optimise Sustainable Land Use Planning in Samin Watershed, Central Java, Indonesia
Bibliographic record
Abstract
This research aims to analyze ecosystem services providing food and water as a basis for sustainable land use planning in the Samin watershed.This type of research is a descriptive survey.This research describes spatially the ecosystem services of providing food and water in the research area, which is processed using a Geographic Information System (GIS) with output in the form of an ecosystem services map.The research results show that the Samin watershed Ecoregion is mostly (54%) in the form of Fluvio-volcanic Plain Pyroclastic material and the land cover is dominated by irrigated rice fields (43.69%) influencing the high level of food supply ecosystem services in the Samin watershed, so that the majority (59%) provision of ecosystem services including food is very high.Most of the Samin watershed water supply ecosystem services (72%) are in the high category, influenced by ecoregional conditions and land cover of the Samin watershed.The land cover of the Samin watershed is mostly (72.96%) in the form of land cover which functions for water absorption, and the density of the land cover is mostly (49.721%) including very high density.The research results can be used as a reference for the government in determining policies towards sustainable land use.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".