Rock dust-based potting media enhances agronomic performance and nutritional quality of horticultural crops
Why this work is in the frame
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Bibliographic record
Abstract
The study evaluates the use of rock dust (RD)-based potting media in enhancing the agronomic performance and nutritional quality of amaranth , kale, and lettuce under controlled environmental conditions. A total of ten growth media formulations, including: 1) 100 % RD (RD), 2) 50 % RD+50 % Topsoil (RDT), 3) 50 % RD + 25 % Biochar + 25 % Promix (RBP) 4) 100 % Topsoil (TS), 5) 25 % RD + 75 % Topsoil (RT), 6) Huplaso (negative control), 7) 50 % RD + 25 % compost + 25 % promix (RCP), 8) 50 % RD + 50 % Promix (RP), 9) Promix (P) (Control), and 10) 50 % RD + 50 % Biochar (RB) were evaluated. The addition of RD to media resulted in a significantly higher root-shoot ratio in amaranth and lettuce. The RCP, RBP, and control showed a significant increase (p<0.05) in total biomass (TBM) and the number of leaves in kale and lettuce during crop cycles. The total antioxidant content of lettuce showed a significant increase in RT>RCP>RD over the control P. Lettuce crops grown in RB had the highest consumer preference based on size and overall appearance. Overall, this study demonstrated an increase in total microminerals, fresh weight, total biomass, MUFA (monosaturated fatty acid), protein content, and antioxidants in plant tissue produced using RD-based media amendments. This is supported by the strong association observed between the media quality and the agronomic performance as well as the nutritional composition. The results suggest RD-based amendments (RCP, RBP, RB, and RP) could be used as suitable, sustainable, and cost-effective media amendments for improving the growth and nutritional composition of vegetable crops , limiting the environmental disposal of RD following precious metal mining. Further optimization of the above media would enhance its utility for vegetable production in different crop management systems.
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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 it