Pelleted manure compost improves mine spoil properties enhancing plant growth and phyto-stabilization of potentially toxic metals
Bibliographic record
Abstract
Feedlot manure is rich in plant nutrients and can immobilize potentially toxic metals. However, pelleted manure compost as an amendment material in mine spoils (chat) is not well studied. This study was conducted to investigate the impact of pelleted cattle manure on improving chat properties facilitating phyto-stabilization and the establishment of grasses. A greenhouse pot experiment was conducted with unamended and amended chat (lime treated) with pelleted manure at three rates (60, 120, and 180 Mg ha−1) with and without bentonite (B), using two native grasses, switchgrass ( Panicum virgatum L.) and wheatgrass ( Pascopyrum smithii (Rydb.) A. Löve). Leachates from pots were collected periodically until harvest. Nutrients and metal concentrations were measured in chat treatments, and metal concentrations were measured in plant tissues and leachates. Manure-amended chat reduced leachate Cd and Zn on average by >75% and >80%, respectively. Above-ground dry matter yield increased by >2.5-fold and >4-fold, respectively, in switchgrass and wheatgrass with the increase of 3-fold manure rate. The manure rate at 180 Mg ha−1 reduced plant Cd and Zn by 50% and 20%, respectively, in wheatgrass, and 30% and no reduction, respectively, in switchgrass, compared to the 60 Mg ha−1 manure rate. Overall, pelleted manure compost significantly increased available nutrients and decreased available metals in amended chat, with no significant effect of B. This study indicated that pelleted manure, preferably at 180 Mg ha−1 rate with lime, can be used in acidic metal-contaminated chat to facilitate the establishment of perennial native grasses and reduce the potentially toxic metal availability.
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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 | 0.000 | 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".