Associations between watershed characteristics, runoff, and stream water quality: hypothesis development for watershed disturbance experiments and modelling in the Forest Watershed and Riparian Disturbance (FORWARD) project
Bibliographic record
Abstract
The FORWARD project, based on the Boreal Plain of Alberta, was initiated to develop models to predict the influence of watershed disturbance on runoff and stream water quality. To generate hypotheses relating to watershed controls on streams in the presence and absence of disturbance, we quantified relationships between stream variables and soil distribution in nine undisturbed small (M = 5.4 km2) watersheds for two relatively dry and snowmelt-dominated seasons (May through October 2002 and 2003). We also considered data from one harvested and two burned watersheds. Among soil types, only peatland cover had an association with runoff and water quality. Runoff and ammonium exports were positively related to peatland cover in both years (r2 = 0.50 to 0.90; P < 0.05). In the first year, additional relationships to peatland cover existed for particulate phosphorus and suspended sediment exports (r2 = 0.64 and 0.65, respectively), whereas in the second year they existed for dissolved phosphorus and dissolved organic carbon exports (r2 = 0.67 and 0.78, respectively). Hypotheses generated relate to the role of peatlands as sources for water moving toward stream channels, water exchange between streams and riparian groundwater, and the influence of disturbance and precipitation patterns on runoff generation. Key words: watershed disturbance, boreal forest, peatland, stream, suspended sediments, nutrients, runoff, forest harvest, wildfire.
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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 | 0.010 | 0.008 |
| 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 | 0.001 | 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".