Evaluation of Legacy Forest Harvesting Impacts on Dominant Stream Water Sources and Implications for Water Quality Using End Member Mixing Analysis
Bibliographic record
Abstract
Forests are critical water supply regions that are increasingly threatened by natural and anthropogenic disturbance. Evaluation of runoff-generating processes within harvested and undisturbed headwater catchments provides insight into disturbance impacts on water quality and drinking water treatability. In this study, an extensive hydrologic dataset collected at the experimental Turkey Lakes Watershed (TLW) located on the Canadian Shield was used to quantify sources of stormflow in legacy clear-cut (24-years post harvesting) and forested (control) headwater catchments using an end member mixing analysis (EMMA) model. Stream water, groundwater, soil water, and throughfall water quality were evaluated during spring snowmelt, stormflow, and fall wet-up. Groundwater chemistry was similar to stream water chemistry in both catchments, suggesting that groundwater is a major contributor to stream flow. The water chemistry in small wetlands within the study catchments was comparable to stream water chemistry, suggesting that wetlands are also important contributors to stream flow. Differences in wetland position between the legacy clear-cut and control catchments appeared to have a greater influence on source contributions than legacy harvesting. Results from this study provide insight into runoff-generation processes that reflect event/seasonal flow dynamics and the impacts on water quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".