Integrating Rock Dust and Organic Amendments to Enhance Soil Quality and Microbial Activity for Sustainable Crop Production
Bibliographic record
Abstract
Rock dust (RD) is a by-product of the precious metal mining industry. Some mining operations produce close to 2,000,000 Mg of RD/year, posing disposal issues. This study evaluated the physicochemical and microbial properties of RD from gold mining and its potential use in RD-based growing media. Ten media formulations were tested: Promix (Control), 100% (RD), 100% topsoil (TS), 50% RD + 50% topsoil (RDT), 25% RD + 75% topsoil (RT), 50% RD + 50% Promix (RP), 50% RD + 25% biochar + 25% Promix (RBP), 50% RD + 25% compost + 25% Promix (RCP), 50% RD + 50% biochar (RB), and Huplaso (negative control). RD particle size ranged from 0.1 to 2 mm with a bulk density of 1.5 g cm−3, while RD-based media ranged from 0.8 to 1.1 g cm−3 showing increased porosity. Nutrient content was analyzed using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), and the active microbial community assessed using PLFA biomarkers via GC-MS/FID, n = 4 and p = 0.05. Microbial analysis identified five classes (protozoa, eukaryotes, Gram-positive (G+), Gram-negative (G−), and fungi (F)), with a significant increase in G−, G+, and F in RD-based amendment RBP (28%) compared to control P (9%). G+, G−, and F showed a strong negative correlation (r = −0.98) with pH, while calcium correlated positively (r = 0.85) with eukaryotes and a strong positive correlation (r = 0.95) of cation exchange capacity with G+. This study suggests blending RD with organic amendments improves physicochemical quality and microbial activity, supporting its use in crop production over disposal.
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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 | 0.001 | 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".