Drainage basin morphometrics for depressional landscapes
Bibliographic record
Abstract
Measures of the size, position, and connectivity of depressional wetlands are related to runoff variations among 12 forested catchments on the Canadian Shield under varying moisture conditions. A fine‐resolution digital elevation model was used to delineate wetlands within the catchments. Analyses showed that wetland metrics as predictors of runoff variation were superior to catchment area and mean slope, two common basin metrics. The most useful metrics were the area of wetlands in bottomland positions, total wetland area, and volume. During wet periods, catchments containing extensive wetlands were marked by a significant decrease in maximum peak discharge and increase in duration of flow. During mesic and dry periods, catchments containing extensive wetlands were marked by an increase in rise and recession times of peak discharge events and the duration of flows. These characteristics resulted from the interaction between wetlands, their hydrologic connectivity to surface flow paths, and runoff.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".