Effects of Coarse Woody Debris on Soil Temperature and Water Content in Two Reconstructed Soils in Reclaimed Boreal Forest
Bibliographic record
Abstract
Coarse woody debris (CWD) and cover soils are used to expedite ecological processes in reclaimed boreal forests after oil sands mining. Soil water content and soil temperature are considered key factors for revegetation during mine reclamation as they impact soil surface and atmosphere interactions and plant growth. However, the effects of CWD and cover soils on soil water content and temperature are not well studied. This study assessed the impact of CWD size (large, small) and type (spruce: Picea mariana, aspen: Populus tremuloides) on soil water content and temperature in two soils constructed with forest floor-mineral mix (FMM) and peat-mineral mix (PMM)) at oil sands reclamation sites. Annual and summer precipitation showed year-to-year variability; mean air temperature did not. Soil cover type had a greater impact on moderating soil water content than CWD, with PMM having a stronger influence on water content and temperature than FMM. Adding CWD increased soil water content more in FMM than PMM, and the effect on soil temperature was mostly observed during the summer months. In PMM, spruce small CWD was associated with greater water content, whereas there was no distinct differentiation between CWD size and type in FFM. This study suggests application of CWD in FMM would be more beneficial than in PMM for reclamation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".