Effects of Stockpiling and Organic Matter Addition on Nutrient Bioavailability in Reclamation Soils
Bibliographic record
Abstract
Core Ideas Stockpiling and organic matter admixing effects on reclamation soils were tested. Stockpiling effects were stronger on forest floor based reclamation soils. Organic matter admixing showed fertility and microbial benefits mostly in mineral sub‐soils. A faster microbial assimilation was achieved in soils admixed with forest floor organic matter. Nutrient bioavailability is crucial for vegetation establishment and organic matter cycling after major ecosystem disturbance such as open pit mining. In this study, the stockpiling and organic matter admixing effects on nutrient bioavailability were examined in soils used for reclaiming oil sands disturbed sites in northern Alberta, Canada. Stockpiled and directly salvaged peat mineral soil mix (PMM) and forest floor mineral soil mix (FFMM), the two main oil sands reclamation soils, and a nutrient poor mineral sub‐soil (SS) were used in this experiment. Reclamation soils were inter‐mixed at different ratios (PMM to FFMM or SS at 60:40, 80:20 and 90:10) to examine the organic matter admixing effects. Significant stockpiling effects on nutrient bioavailability and microbial functions were mostly observed in FFMM. Microbial biomass C was greater, and mineralization of lignin substrate was lower in both stockpiled PMM and FFMM soils compared to the directly salvaged soils. Significant fertility benefit was found in the FFMM‐admixed SS and PMM soils through an increase in N and K availability. FFMM admixing also increased microbial functional diversity and assimilation rate compared to the non‐admixed soils. Mineralization of polymeric substrates was the main driver of nutrient availability in stockpiled PMM, whereas carboxylic acids and carbohydrates were the major drivers in directly salvaged PMM, as indicated by the Random Forest models. The findings suggest that stockpiling effects are much stronger in FFMM than in PMM, and FFMM admixing to reclamation soils may provide nutritional and microbial functional benefits, especially in nutrient‐poor 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".