Validation of the Canadian Precipitation Analysis (CaPA) for Hydrological Modelling in the Canadian Prairies
Bibliographic record
Abstract
Traditional hydrological model inputs are often deemed inadequate in areas where stations are sparse, such as the northern extents of the Canadian Prairie basins. The Canadian Precipitation Analysis (CaPA) combines GEM (Global Environmental Multi-scale model) data and available observation data to provide enhanced precipitation estimates. The CaPA analysis has recently been extended to produce high-resolution precipitation data over the Canadian Prairies, encompassing the Nelson-Churchill River Basin. Manitoba Hydro and other water practitioners in Manitoba have expressed interest in potentially using CaPA precipitation as hydrological model forcing for Prairie watersheds. A three step validation approach was designed and applied to assess CaPA for hydrologic modelling applications in the Nelson-Churchill River basin. Results of validation show that the quality of CaPA data varies among regions and seasons, with CaPA proving beneficial in both data-sparse regions and winter seasons most prominently. Overall, CaPA shows promise for water resource application in the Canadian Prairies.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".