Simulating cold-region hydrology in an intensively drained agricultural watershed in Manitoba, Canada, using the Cold Regions Hydrological Model
Bibliographic record
Abstract
Abstract. Eutrophication and flooding are perennial problems in agricultural watersheds of the northern Great Plains. A high proportion of annual runoff and nutrient transport occurs with snowmelt in this region. Extensive surface drainage modification, frozen soils, and frequent backwater or ice damming impacts on flow measurement represent unique challenges to accurately modeling watershed scale hydrological processes. A physically-based, non-calibrated model created using the Cold Regions Hydrological Modelling platform (CRHM) was parameterized to simulate hydrological processes within a low slope, clay soil, and intensively surface drained agricultural watershed. These characteristics are common to most tributaries of the Red River of the North. Analysis of the observed water level records for the study watershed (La Salle River) indicate that ice cover and backwater issues at time of peak flow may impact the accuracy of both modeled and measured stream flows, highlighting the value of evaluating a non-calibrated model in this environment. Simulations best matched the streamflow record in years when peak and annual discharges were equal to or above the medians of 6.7 m3 s−1 and 1.25 × 107 m3, respectively, with an average Nash-Sutcliff efficiency (NSE) of 0.76. Simulation of low-flow years (below the medians) was more challenging (average NSE
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".