Connectivity and storage functions of channel fens and flat bogs in northern basins
Bibliographic record
Abstract
Abstract The hydrological response of low relief, wetland‐dominated zones of discontinuous permafrost is poorly understood. This poses a major obstacle to the development of a physically meaningful meso‐scale hydrological model for the Mackenzie basin, one of the world's largest northern basins. The present study examines the runoff response of five representative study basins (Scotty Creek, and the Jean‐Marie, Birch, Blackstone and Martin Rivers) in the lower Liard River valley as a function of their major biophysical characteristics. High‐resolution (4 m × 4 m) IKONOS satellite imagery was used in combination with aerial and ground verification surveys to classify the land cover, and to delineate the wetland area connected to the drainage system. Analysis of the annual hydrographs of each basin for the 4 year period 1997 to 2000, demonstrated that runoff was positively correlated with the drainage density, basin slope, and the percentage of the basin covered by channel fens, and was negatively correlated with the percentage of the basin covered by flat bogs. The detailed analysis of the water‐level response to summer rainstorms at several nodes along the main drainage network in the Scotty Creek basin showed that the storm water was slowly routed through channel fens with an average flood‐wave velocity of 0·23 km h −1 . The flood‐wave velocity appears to be controlled by channel slope and hydraulic roughness in a manner consistent with the Manning formula, suggesting that a roughness‐based routing algorithm might be useful in large‐scale hydrological models. Copyright © 2003 Crown in the right of Canada. Published by 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 | 0.001 | 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".