Development of Superior Plantation Commodities Based on Sustainable Development
Bibliographic record
Abstract
Riau is an Indonesian province known for its large plantations. The plantation business makes the most significant contribution to the fiscal revenues of the province. This paper aims to innovate rural economy through the sustainable development of superior commodities in the plantation sub-sector of agriculture. Specifically, multi-criteria analysis (MCA) was performed to determine the superior commodities in each locality. The results show that the superior commodities include palm oil, rubber, coconut, cocoa, coffee, and sago. The development of these commodities could boost farmers’ income with the added value of planation products. On this basis, the fair use of land for all categories was highlighted from social, economic, and environmental perspectives, and recognized as essential to the development of sustainable plantations, which are integral to rural economy and land sustainability. The proposed development strategy for superior commodities in plantation is in line with the sustainable development goals (SDGs) in Riau Province, Indonesia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".