Silicon Release from Local Materials in Indonesia under Submerged Condition
Bibliographic record
Abstract
Five inorganic materials (steel slag, silica gel, electric furnace slag, fly ash and Japanese silica fertilizer) and six organic materials (rice husk-biochar, rice straw compost, media of mushroom, cacao shell-biochar, rice husk-ash and elephant grass), were evaluated as Si fertilizer sources for rice plants (Oryza sativa L.) in two soil types (red clayey and sandy soil). Evaluation was carried out by incubating them at 30oC under submerged condition for 70 days. The soil solution was replaced at day 7, 14, 21, 42, 49, 56, 63 and 70 and the amount of silicon (Si) release, pH, Eh, calcium (Ca), magnesium (Mg), iron (Fe) and manganese (Mn) concentrations in soil solutions were determined. The amount of Si release ranged from n.d. (not detected)-32444.7 mg Si kg-1 and 105.84-48524.0 mg Si kg-1 in red clayey and sandy soil solutions, respectively during 70 days of incubation. Reduction in soil Eh was accompanied with an increase in the solubility of the soil Si especially for silica gel, electric furnace slag, elephant grass and media of mushroom. Higher exchangeable Ca content in soil tended to suppress Si release from rice straw compost, rice husk-ash and cacao shell-biochar. Considering the results of present study and availability of the materials, we concluded that steel slag of the inorganic materials and rice straw/husk and cacao shell-biochar of organic materials had the highest potential as Si fertilizer source in Indonesia.
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 | 0.000 |
| 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) | 0.001 | 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 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".