PEMETAAN POTENSI KERENTANAN PENCEMARAN AIR PERMUKAAN UNTUK PENGENDALIAN SANITASI LINGKUNGAN DI KABUPATEN BULELENG (Mapping on the potential vulnerability of surface water pollution for environmental sanitation control in Buleleng Regency)
Bibliographic record
Abstract
One of the efforts to prevent water pollution is done by mapping of potential pollution vulnerability to support water quality control policy making. The research location is in Panas Sub-watershed, a part of the Saba Watershed which originates on the northwest slope of the Bratan Volcano Complex and upstream is Lake Tamblingan, Buleleng Regency. The objectives of the study include: 1) mapping the parameters of potential surface water pollution vulnerability, 2) mapping land cover changes in 2000 and 2016, 3) mapping the potential surface water pollution vulnerability and its dynamics in 2000 and 2016, and 4) formulating a management recommendation to control surface water pollution. Mapping the potential surface water pollution vulnerability was conducted by GIS and Point Count System Model (PCSM) method using parameters of slope, land cover, and annual average rainfall. The results showed that some of the upstream and middle areas of the Panas Sub-watershed were categorized as high potential vulnerability caused by steep slopes, plantation cover, and annual average rainfall of 2,251 - 2,500 mm/year. Various recommendations to control surface water pollution are conducting integrated waste management individually or in groups, such as collectively septic tank construction, waste disposal organization, and waste water management installation; providing directions for appropriate land cultivation for farmers so that the pollutant load due to the use of pesticides could be controlled; as well as controlling and supervising the tourist area around Tamblingan Lake.
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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 | 0.000 | 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) | 0.005 | 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".