Building a Better Soil for Upland Surface Mine Reclamation in Northern Alberta: Admixing Peat, Subsoil and Peat Biochar in a Greenhouse Study with Aspen
Bibliographic record
Abstract
Surface mining of oil sands in northeastern Alberta is a large-scale disturbance affecting over 900 km2 so far. Extraction companies are required by law to return the environment to “equivalent land capability”, but this has been challenging to quantify. To date, only one site has been certified as reclaimed. Restoring ecosystem function, including nutrient availability and uptake, might be a more realistic goal of reclamation. We tested the effect of admixing subsoil with peat and peat biochar on bioavailable nutrients, foliar nutrient concentration, and aspen (Populus tremuloides Michx.) productivity in a greenhouse study. Brunisols and luvisols, found in upland boreal forests of the Athabasca oil sands region, have high mineral soil content compared with the commonly used peat. Charcoal is a native component of boreal forest soils in northern Alberta and affects a variety of soil characteristics. In two separate tests, we compared different peat–subsoil admixtures and biochar-amended peat–subsoil admixtures with forest floor–mineral mix (FFM). Seedling productivity increased with admixing subsoil in both experiments with and without biochar, and there was an overall positive effect of amendment with biochar when comparing all treatments of both experiments using multivariate statistics, with biochar being more similar to FFM. Our findings suggested that peat–subsoil mixes did not provide sufficient amounts of P and Cu to seedlings. A lower K and Mn availability in peat–subsoil mixes was also identified and needs to be evaluated in further studies.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".