Hydrologic modelling on the Saint Esprit watershed
Bibliographic record
Abstract
A study was undertaken to evaluate the suitability of the SLURP hydrological model for simulating the hydrology of the Saint Esprit watershed (26 km 2) in Quebec. Climatic data and other input were made available through a monitoring program set up in the watershed from 1994 to 1998. GIS was used to store, analyze and export the watershed information into the model. The continuous semi-distributed model SLURP was calibrated using three years of data (1994--1996). Parameter calibration, except that of snowpack melt-rate, was done through an automatic optimization technique. The model was validated using graphical outputs, the Nash/Sutcliffe (R2) coefficient of performance for daily runoff, and the percent difference of predicted versus computed runoff on a monthly, seasonal and annual basis. Additionally, the evapotranspiration (ET) component of the model was compared with an ET estimated using the Baier & Robertson model (BR) calibrated for the region. (Abstract shortened by UMI.)
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".