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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".