The impact of soil moisture availability on forest growth indices for variably layered coarse‐textured soils
Bibliographic record
Abstract
ABSTRACT The reestablishment of productive forests over mining waste and overburden is a primary reclamation goal in oil sands mining in Northern Alberta, Canada. Soil water conditions in coarse‐textured soils can be limiting to forest growth. The objective of this study was to evaluate the effect that textural variability may have on plant‐available water and concomitant forest productivity on coarse‐textured reclamation soils. The ecophysiological and biogeochemical processes model, Biome‐BGC (Thornton et al., Agricultural and Forest Meteorology 113: 185–222, 2002), was employed to simulate forest dynamics. The water flow sub‐model in Biome‐BGC was replaced by a field‐validated physically based formulation for transient unsaturated water flow. The modified model was assessed using validated physiological parameters, and model predictions were compared with measurements of aboveground biomass dynamics for jack pine (Pinus banksiana Lamb), white spruce [Picea glauca (Moench) Voss], and trembling aspen (Populus tremuloides Michx.). The modified Biome‐BGC model was then used to evaluate the response of leaf area index and net primary production to available water holding capacity on texturally variable, coarse‐textured soils. The results indicate that textural variability could increase the available water holding capacity within a 1‐m profile of coarse‐textured soil by 8 to 16 mm. This enhanced available water holding capacity could increase forest leaf area index by 0·3 to 0·8 and net primary production by 14–30% depending on the specific soil texture and tree species. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".