Developing manufactured soils from industrial by‐products for use as growth substrates in mine reclamation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Suitable soils for reclamation can be acquired through excavation and translocation of local soils, increasing the industrial footprint on previously undisturbed lands and causing negative environmental impacts. Manufactured soils (Technosols) could be a viable soil source when the availability of suitable natural soils is limited. The purpose of this study was to manufacture a Technosol from an admixture of woody residuals, primary paper sludge, and two subtypes of nonacid generating crushed mine rock, to function as a growth substrate for revegetation of mined land. Technosols manufactured with 0, 25, 50, and 75% organic materials (v/v) were assessed in a 10‐week growth study using annual ryegrass biomass production and allocation as a performance indicator. Technosols containing no organic materials had significantly lower plant nutrient concentrations than Technosols containing an organic constituent and, after 5 weeks of growth, ryegrass grown on nonorganic Technosols had greater root:shoot ratios than ryegrass grown on organic Technosols. Organics increase the water holding capacity and nutrient concentrations of Technosols and should be included in manufacturing Technosols for revegetation. Technosols manufactured with primary paper sludge produced lower shoot biomass than Technosols manufactured with woody residuals, which could be in part due to the higher pH of the paper sludge. Technosols can be manufactured for revegetation purposes and individual components should be assessed before and after mixing. Further development of Technosols should include field testing and amendment or fertilizer use to improve soil nutrient content.
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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