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.
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 | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".