Enhancement of the three-dimensional interfacial layer of a rocky desertification soil using a red mud-based fertilizer
Bibliographic record
Abstract
Red mud, a solid waste of alumina extraction from bauxite, was used as a compost carrier to prepare a geological fertilizer. It was amended at proportions of 0, 5%, 10%, 15% and 50% by weight (g/kg) to improve a rocky desertification soil (classified as lime soil) productivity. Through the simulation of different rain intensity (15, 50, and 90 mm/h) with three precipitation rates (1000, 2000, 3000 mm), soil chemical and physical properties, such as soil organic matter (SOM), total nitrogen (TN), ammonia nitrogen (AN), nitrate nitrogen (NN), total potassium (TK), available potassium (AK), total phosphorus (TP), available phosphorus (AP), bulk density and aggregates were tested and analyzed. In addition, a three-dimensional evaluation and analysis of the improvement attributed to the geological fertilizer was conducted. The results showed that the soil loss could be maintained in the range of 19%–72% under rainfall intensities. In addition, the reduction rate of soil clay content was less than 20%, and the lowest reduction rate of SOM, TN, TP and other nutrient was only 4% at the application rate of 5%–50%. The BD of the 0–20 cm top soil decreased progressively from 1.2 to 0.9 g/cm3, while the water-stable aggregate volume increased by 45%–76%. The red mud-based fertilizer enhanced the ability of the rocky desertification soil to resist rainfall erosion and infiltration in amended soil profiles. Considering the trends of nutrient losses and effects on the soil structure, the application rate of 15% by weight (g/kg) was best for improving the rocky desertification soil productivity.
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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) | 0.000 | 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".