3 Improved Mapping of Snow Water Equivalent over Quebec
Bibliographic record
Abstract
This extended abstract describes initial results from a project to develop 10-km resolution gridded maps of SWE over Québec from historical surface observations of snow depth and SWE. The project has assembled an historical snow course database for Quebec and surrounding regions containing ~145,000 observations covering the period 1936-2006. The SWE observations are interpolated to a 10 km grid using the method of kriging with external drift (KED) following Tapsoba et al. (2005) with topography and estimated SWE as external drift variables. The estimated SWE field is generated using precipitation from the CANGRD product (Milewska et al., 2005) and 6-hourly air temperatures from the NCEP reanalysis. The results of an initial evaluation showed the KED method provided spatially realistic SWE fields over Quebec with a number of improvements over the optimal-interpolation approach used in Brown et al. (2003). While the distribution of the available surface observations is quite variable in space and time there are sufficient observations to generate SWE maps for most of Quebec from about the mid-1960s for the 1st and 15th of the month from December to June.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".