Bibliographic record
Abstract
South Sulawesi Province, with the commitment and collaboration of the parties, needs to optimize further the transformation towards green industrial growth for the management of natural resources that ensure environmental sustainability and equal livelihoods for all levels of society. The Green Industry Index is a benchmark to evaluate the achievements and effectiveness of the transformation of Indonesian Industry towards a green Industry. The main principle of Green Industry is to create high industrial growth, along with encouraging social welfare and maintaining industrial quality and environmental carrying capacity. The Green Economy Index (GEI) or the Indonesian Green Industry Index consists of 15 indicators covering three pillars: Industrial, social and environmental. The policies that need to be in place implemented in the implementation of green industry in South Sulawesi are controlling land conversion through increasing the capacity of farmers by implementing climate change agriculture technology or climate-smart agriculture. Providing environmental services in the form of assistance or incentives for farmers who cultivate plants by maintaining the sustainability of the carrying capacity of their land or who contribute to improving environmental quality. Development of downstream natural resources using technology that does not harm the environment or surrounding communities and can provide high-added product value for farmers.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".