Evaluation of municipal solid waste (MSW) compost as a soil amendment for acidic, metalliferous mine tailings
Bibliographic record
Abstract
Four 0·5 ha plots were established on freshly dried tailings of INCO Ltd. near Sudbury, Ontario, Canada and received the following treatments: 1) 125 t/ha Municipal Solid Waste (MSW) compost, 2) 250 t/ha MSW compost, 3) INCO's standard application of crushed limestone, fertilizer and hay mulch and 4) control - no application of compost. Application of compost at a rate of 250 t/ha yielded the same increase in tailings pH (from 2·80 to 4·65) as that achieved with the typical INCO application of 40 t lime/ha. The INCO treatment with lime and fertilizer resulted in no initial input of organic matter; whereas, a 250 t/ha application of compost increased organic content by 8·3%. Moisture retention in tailings at the two compost-treated plots was significantly higher (26%) than that of the INCO treated plots (<10%). Application of lime as part of the INCO treatment reduced levels of water soluble Cu and Ni from 22·8 pgCu/g to 0·4 pgCu/g and from 35·5 pgNi/g to 5·2 ngNi/g. Similar reductions in water soluble copper and nickel in tailings were achieved with the 250 t/ha application of compost. It was concluded that tailings amelioration with MSW compost is superior to the INCO treatment because it more rapidly increases pH, moisture content and organic content, and reduces concentrations of water soluble (plant available) Cu and Ni.
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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".