Bio-organic-mineral fertilizer can improve soil quality and promote the growth and quality of water spinach
Bibliographic record
Abstract
Increasingly, poor soils are used for plant cultivation involving chemical fertilizer (CF) usage which in turn lowers soil quality. To improve the quality of poor soils, promote plant growth, and reduce CF use, bio-organic-mineral fertilizer (BF) was introduced to poor soil to examine its effects on both soil and plants. Organic fertilizer (OF), CF, and mineral powder (MP) were applied as controls. When cotreating water spinach with fertilizer, the soil quality was improved with the increased doses of BF, OF, and MP. The BF treatment produced the most pronounced improvement. Applying CF reduced the soil quality. Under the CF treatment the largest plant-growth-promoting effect was observed within 30 d, whereas plants treated with BF showed the most favorable growth after 50 d. Moreover, for BF, plants’ nutritional quality was also higher, particularly in the long term. When poor soil was treated with fertilizer without plants, the soil quality changed as in the previous treatment; however, the change in this treatment was more pronounced and the remaining available elements in soil were higher, particularly in the case of CF treatment. These results indicate that BF treatment without CF can be applied to improve the quality of poor soil, and meanwhile promote plant growth and quality.
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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".