Bibliographic record
Abstract
Currently, snow analysis for weather prediction in Canada is conducted using snow depth measurements alone. The current effort is intended to revisit this analysis using both snow depth and density measurements from snow course sites previously unused during weather prediction analysis. The purpose of this reanalysis is to produce a gridded daily Snow Water Equivalent (SWE) hindcast within the transect from the Great Lakes through Quebec and into Labrador. -- The final SWE prediction was produced by combining output from existing deterministic snow density models and developed statistical prediction models. Statistical Models were developed based on Universal Kriging (UK) interpolation technique on measured data and by considering the background fields. These fields include a number of physiographic variables and the Canadian Meteorological Centre (CMC) snow depth analysis product. -- Finally, the new product was evaluated from the calculated Root Mean Square Error (RMSE) of the predicted SWE at both validation and cross validation points, simulation vs. observation comparison, and also from a visual consistency check. The research produced a methodology for SWE prediction and daily gridded SWE product, which is a valuable attempt to improve hydrological prediction from snow melting. The average RMSE of SWE prediction was around 30-35 mm although, the validation of results were challenged by limited quantity of snow course data.
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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 | 0.000 | 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) | 0.004 | 0.001 |
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".