Amendments and substrates to develop anthroposols for northern mine reclamation
Bibliographic record
Abstract
Mining and natural resource development in the Canadian north has produced large areas of disturbance and volumes of waste, necessitating reclamation. This study focused on building anthroposols in a greenhouse using waste material from Diavik Diamond Mine. There were six substrates (crushed rock, lakebed sediment, processed kimberlite, and its combinations), seven organic amendments (sewage, soil, peat, Black Earth, biochar, and its combinations), a control, and two nutrient treatments (with and without fertilizer). Substrates and amendments were mixed and seeded with three grass species. Soil properties and vegetation responses were assessed. Substrate structure was a challenge; crushed rock and processed kimberlite had little fine material and lakebed sediment was compacted. Processed kimberlite and sewage had metal concentrations (barium, chromium, cobalt, copper, molybdenum, nickel, selenium, and zinc) above guidelines. Vegetation established on all anthroposols, with plant growth and density greatest in crushed rock, followed by 25% processed kimberlite with 75% lakebed sediment and 100% lakebed sediment. Substrates amended with peat and (or) soil had the greatest plant density and belowground biomass; substrates amended with sewage and sewage–soil had greatest aboveground biomass. Fertilizer had a limited effect. With appropriate amendments, waste materials at the mine showed potential as reclamation soils.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".