A combined field data and empirical modeling approach to precipitation-runoff analysis in an agro-forested Prairie watershed
Bibliographic record
Abstract
Low relief, heavily human-impacted landscapes like those of the Prairies in south-central Canada have received little attention in previous hydrological research. Here, the rainfall-runoff relationship in the context of both a field-based investigation and an empirical model is examined in an effort to provide insight into Prairie hydrology. Rainfall and water level data were collected for nested sub-watersheds of the Catfish Creek watershed, a 642 km2, near-level, mixed land use and engineered Prairie watershed. First, the dataset is examined for runoff controls. Second, the history of the United States Curve Number Method is reviewed and its initial abstraction ratio examined against collected field data to determine the applicability of a single, constant ratio to Prairie landscapes. Overall, the results indicate that Prairie runoff generation processes differ significantly from those of humid, pristine catchments of higher relief and a conceptual model is proposed with that regards.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".