KAJIAN LINGKUNGAN HIDUP STRATEGIS KAWASAN PERUNTUKAN PERTAMBANGAN DI KABUPATEN BOLAANG MONGONDOW
Bibliographic record
Abstract
This qualitative study was analyzed using semi-detailed method is based on the assessment of data and information that is more accurate, can be quantitative. Primary data was collected through interviews with the stakeholders starting from the government officials, the academics and community leaders to get information about important issues emerging in Bolaang Mongondow. Secondary data collected from various government agencies and institutions or personnel associated with the research. The results showed that the mining activities in BolaangMongondow both for exploration and exploitation cause negative impacts by 50%. Starting from the conversion of land that have growing increasingly by time which can certainly bring problems in the future. The implication of uncontrolled agricultural land conversion can threaten food supply capacity and even in the long term can cause social harm. The mining activities, plantation and cultivation will lead to fragmentation of forest landscapes. Forest fragmentation will cause the forest to be smaller in which to live for animals and resulted in less food available for wildlife. There are 6 rivers in Bolaang Mongondow indicated that some parameters that are already above the value of standard quality. They are River Toraut located in the Sub-district of West Dumoga, Totabuan River, Tombolango River located in the Lolak Sub-district, upstream and downstream sections of Wineru River and Nonapan River located in Poigar Sub-District. In addition, natural disasters often occur in Bolaang Mongondow including floods and landslides. This natural disaster resulted in tremendous losses for the community. This study concludes that need to enactment of Regions to regulate mining activities in the area. And also there are should be intensive supervision from the institution concerned. The most important thing is also necessary accomplishment of an alternative control strategies are based on community participation.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".