Developing manufactured soils from industrial by‐products for use as growth substrates in mine reclamation
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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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) | 0.001 | 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".