A LITERATURE STUDY OF BENEFITING K-BEARING SILICATE ROCKS AS RAW MATERIALS FOR POTASSIUM FERTILIZER
Bibliographic record
Abstract
As an agricultural country Indonesia requires NPK fertilizer up to 2.6 million tons per year. However, such a number is mostly fulfilled by imports, particularly potassium (K) fertilizer. Almost a 100% of K-fertilizer comes from Canada and Russia in the form of KCl (sylvite) salt. Indonesia does not have sylvite mineral, but retains some K-bearing minerals such as K-feldspar and leucite. Both are different in characteristics from sylvite. K-feldspar and leucite are the alumino-silicate minerals. They require special treatment to process them into K-fertilizer. Several techniques can be applied to process both minerals, such as by mechano-chemistry, leaching, alkali fusion and bioleaching. Research on the utilization of K-source minerals as a raw material for K fertilizer is rela- tively rare. The opportunity to conduct such a research is widely open, as currently conducted by the Research and Development Centre for Mineral and Coal Technology.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".