TIME-SERIES EXPANSION OF OIL PALM PLANTATION IN PULANG PISAU REGENCY
Bibliographic record
Abstract
The primadonna of oil palm is unavoidable as the largest commercially cultivated crop in tropical regions, especially Indonesia. The escalation in oil palm demand aligns with Land Use Land Cover (LULC) conversion and the transformation of global agricultural landscape. Expansion in Kalimantan proves that the oil palm areas increased from 90,300 ha to 3,164,000 ha, with 90% of the converted land being forest areas. Complexity of these issues drives need to conduct research on oil palm plantation expansion in Pulang Pisau Regency. This study presents the manifestation of LULC conversion into oil palm plantations through remote sensing. The results show the expansion of oil palm plantations from 2009-2023, non-compliance with applicable spatial regulations, and changes in the agrarian structure of the surrounding communities. This analysis can serve as a decision-making tool for stakeholders in the oil palm sector that is effective and efficient in terms of cost, time, and effort. Keywords: Spatial Analysis, Oil Palm, Community, Government Regulation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".