Effects of aspen harvesting on groundwater recharge and water table dynamics in a subhumid climate
Bibliographic record
Abstract
Numerical experiments were developed using different water table depths and soil textures to investigate the impact of aspen harvesting on hydrological processes on the Western Boreal Plain. The effect of harvesting on soil moisture dynamics, fluxes at the water table, and water table fluctuation were compared for different harvesting scenarios simulated under wet and dry climatic cycles. Strong interaction between shallow water tables (i.e., 2 m) and atmospheric variability is observed for all soil textures and is reduced as the vadose zone thickens, particularly after a dry cycle, as a series of positive net atmospheric fluxes are needed to reduce soil moisture storage in order for recharge to occur. Because of harvesting, the water table fluxes can increase by 50 mm month−1, while on a yearly basis, this increase can reach 200 mm yr−1, with rainfall events taking between 1 and 5 years to become recharge (i.e., time lag). Also, the water table is expected to rise between 1 and 3.5 m, with rainfall–water table rise time lags of 1–3 years; however, the peak manifestation of harvesting on water table elevation can take up to 7 years after harvesting. The effects of aspen harvesting are more pronounced during wet cycles, and the development of forestry activities in the Boreal Plain should consider not only preceding precipitation but also the preceding precipitation–reference evapotranspiration ratio, water table depth, and soil texture. The interaction of these factors needs to be considered in order to develop sustainable forestry plans and avoid waterlogging conditions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".