Nitrogen Mineralization and Microbial Activity in Oil Sands Reclaimed Boreal Forest Soils
Bibliographic record
Abstract
Organic materials including a peat-mineral mix (PM), a forest floor-mineral mix (L/S), and a combination of the two (L/PM) were used to cap mineral soil materials at surface mine reclamation sites in the Athabasca oil sands region of northeastern Alberta, Canada. The objective of this study was to test whether LFH provided an advantage over peat by stimulating microbial activity and providing more available nitrogen for plant growth. Net nitrification, ammonification, and N mineralization rates were estimated from field incubations using buried bags. In situ gross nitrification and ammonification rates were determined using the 15N isotope pool dilution technique, and microbial biomass C (MBC) and N (MBN) were measured by the chloroform fumigation-extraction method. All reclaimed sites had lower MBC and MBN, and lower net ammonification and net mineralization rates than a natural forest site (NLFH) used as a control, but the reclamation treatment using LFH material by itself had higher gross and net nitrification rates. A positive correlation between in situ moisture content, dissolved organic N, MBC, and MBN was observed, which led us to conduct a moisture manipulation experiment in the laboratory. With the exception of the MBN for the L/S treatment, none of the reclamation treatments ever reached the levels of the natural site during this experiment. However, materials from reclamation treatments that incorporated LFH showed higher respiration rates, MBC, and MBN than the PM treatment, indicating that the addition of LFH as an organic amendment may stimulate microbial activity as compared to the use of peat alone.
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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 | 0.001 | 0.000 |
| 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".