The Fill–Spill Hydrology of Prairie Wetland Complexes during Drought and Deluge
Bibliographic record
Abstract
Abstract The fill–spill of surface depressions (wetlands) results in intermittent surface water connectivity between wetlands in the prairie wetland region of North America. Dynamic connectivity between wetlands results in dynamic contributing areas for runoff. However, the effect of fill–spill and the resultant variable or dynamic basin contributing area has largely been disregarded in the hydrological community. Long‐term field observations recorded at the St. Denis National Wildlife Area, Saskatchewan, allow fill–spill in the basin to be identified and quantified. Along with historical water‐level observations dating back to 1968, recent data collected for the basin include snow surveys, surface water survey and production of a light detection and ranging–derived digital elevation model. Data collection for the basin includes both wet and dry antecedent basin conditions during spring runoff events. A surface water survey at St. Denis in 2006 reveals a disconnected channel network during the spring freshet runoff event. Rather than 100% of the basin contributing runoff to the outlet, which most hydrological models assume, only approximately 39% of the basin contributes to the outlet. Anthropogenic features, such as culverts and roads, were found to influence the extent and spatial distribution of contributing areas in the basin. Historical pond depth records illustrate the effect of antecedent basin conditions on fill–spill and basin contributing area. A large pond at the outlet of the St. Denis basin, which only receives local runoff during dry years when upstream surface storage has not been satisfied, has pond runoff volumes that increase by a factor of 20 or more during wet years when upstream antecedent basin surface storage is satisfied and basin‐wide runoff contributes to the pond. Copyright © 2011 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".