Response of three native grass species on dry tailings reclamation substrate amended with petroleum coke
Bibliographic record
Abstract
Surface mining around the world has produced large quantities of waste materials with ecological impacts. Oil sands mining in Canada generates large volumes of petroleum coke and tailings every year, which are stockpiled in the mining areas and must be reclaimed through capping or used in reclamation substrates. A greenhouse study was conducted to determine whether substrates of various mixes of dry tailings (DT), tailings sand (TS), and peat mineral mix (PMM) with coke amendment would support emergence and growth of three grass species commonly used in land reclamation. After 16 wk in the greenhouse, plant performance varied with substrate and amendment. Treatment with DT/TS/PMM (50:25:25) with 40% coke had greatest cover, biomass, and density and the best plant health. Plant growth was inhibited in treatments with DT (100%) and coke (100%) due to high concentrations of hydrocarbons, undesirable exchangeable ions, and salinity that restricted soil water retention (gravimetric water content, hydraulic conductivity) relative to treatments with PMM. Agrostis scabra Willd. and Festuca saximontana. showed poor growth or did not survive on DT and DT/TS with coke. Elymus trachycaulus (Link) Gould ex Shinners performed better in all DT mixes and performed the best in DT/TS/PMM with 40% coke. Results from this study indicate that mixing with PMM could improve the reclamation potential of DT and coke.
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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 | 0.000 | 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".