Utilization of Peatlands Based on Local Wisdom and Community Welfare in Riau Province, Indonesia
Bibliographic record
Abstract
The majority of regions in Riau Province are coastal areas and many communities live on land that is dominated by peat. Peat ecosystems have unique characteristics, as they are classified as wetland areas, while also possessing regions of terrestrial land. Poor maintenance of these ecosystems can cause a variety of issues such as forest fires, drought, flooding, biodiversity loss, increasing emissions, climate change, and social community changes. The purpose of this paper is to formulate a model of peatland utilization based on local wisdom and community welfare, in an effort to support the sustainable management of peat ecosystems in Riau Province. The main commodities of the coastal community are agriculture, coconut, rubber, oil palm, sago, coffee, cocoa, areca nut. Therefore, an understanding of the use of peatlands is needed. This is to prevent damage to the peatland ecosystem, maintain biodiversity, store carbon, produce oxygen, and manage water. Policies and strategies for managing the peat ecosystem are carried out through the development of socio-economic and community culture to realize people's welfare based on local wisdom. The management and use of peatlands have contributed to the economy, even as the main source of livelihood for coastal communities. Utilization of peatlands based on local wisdom can maintain a sustainable peat ecosystem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".