Buried wood effects on macronutrient supply and microbial activity and metabolic potential in different oil sands reclamation soils in northern Alberta
Bibliographic record
Abstract
Buried wood is an important yet understudied component of natural and anthropogenic soils. Nutrient immobilization as a response to wood addition during oil sands' reclamation may be a concern since surface wood is salvaged with the soil, thereby becoming buried wood in reclamation cover soils. This project investigated the impact of buried wood on macronutrient supply and microbial communities in different reclamation soils. A 60-day incubation was performed with different rates and types of wood (0%–50%, aspen and pine) and four different soils: fine and coarse forest floor-mineral mix (fFFMM and cFFMM), peat-mineral mix (PMM), and peat. Analysis of macronutrient supply rates using Plant Root Simulator (PRS™) probes and a community-level physiological profiling (CLPP) to assess metabolic potential was performed at the end of the incubation period; microbial activity was measured through soil respiration during the incubation. Responses varied by soil type; however, buried wood caused nitrogen immobilization in three soils due to an increase in the C:N ratio. Soils with lower C:N ratios like fFFMM and PMM were more susceptible to immobilization with a decrease in available nitrogen by up to 95% at a 10% of wood addition. Phosphorus immobilization was observed in cFFMM, and potassium supply increased at 20% of wood and above. Soil microbial activity and metabolic potential increased but no significant changes in the soil profiles were observed. The findings of this study demonstrate that buried wood increases the soil C:N ratio and can potentially cause nitrogen immobilization when added by 10% of volume or more.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".