Organic matter accumulation in reclaimed soils beneath different vegetation types in the Athabasca oil sands
Bibliographic record
Abstract
As of 2008, 60,234 ha of boreal forest had been disturbed by strip mining in the Athabasca Oil Sands in northern Alberta. Reclamation of this area is proceeding concurrently with mine operations, and 6,687 ha are considered to be reclaimed by industry (Hrudey et al. 2010). My study focused on understanding how vegetation planting prescriptions affect soil organic matter (SOM) accumulation in the mineral section of peat/mineral mix (PMM) reclaimed soils. SOM concentration is important in reclaimed soils, as it increases soil fertility and is directly related to site productivity (Farnden et al. 2013). Reclaimed sites which are not particularly wet or dry are planted with either deciduous or spruce trees, and in some cases, grasses. I assessed SOM accumulation, and its source, in different vegetation treatments. Three research questions were developed: (1) Does organic matter content of mineral soil differ between reclaimed and natural soils? (2) What are the dominant sources of organic matter accumulation in the mineral soil of each reclamation treatment? (3) Has SOM accumulated quickest in soils under replanted deciduous (Populus tremuloides/balsamifera), spruce (Picea glauca) or grasses? Seventeen sites were studied, 4 each of the reclaimed deciduous and grassland, 5 reclaimed spruce, and 4 natural forest analogues. At each site, vegetation, forest floor and soils were surveyed. In the laboratory, soil samples from four depths at each site were tested for several properties, including organic matter concentration. The SOM content of all reclaimed soils was significantly higher than the natural analogues. The mechanisms by which SOM accumulates differed for each vegetation treatment: dissolved organic matter and macrofaunal activity were the dominant sources of SOM in deciduous sites; root litter and macrofaunal activity were the dominant sources of SOM in the grassland sites; there was no sign of SOM accumulation at the spruce sites. SOM accumulated quickest in the deciduous sites, intermediate in the grassland sites, and not at all in the spruce sites.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".