Results from model comparisons with ERS-1 and field data for snow water equivalent estimation
Bibliographic record
Abstract
An operational methodology for monitoring snow cover, particularly snow water equivalent (SWE), for a given watershed by merging information coming from RADARSAT images, snow surveys and a hydrological model is under development at INRS-Eau. A first model, which links the scattering coefficient to the physical parameters of the snow cover and the underlying soil has been developed. However, in order to understand better the various processes and their relationships, it was found necessary to apply more specific and detailed models describing each of the processes identified in that general model. A snow accumulation and melt model is being used to simulate the temperature profile both in the snow cover and in the underlying soil, as well as the density and particle diameter of the snow layers. Surface scattering from agricultural surfaces is also being modeled by one of the three usual models (small perturbations, physical optics or geometrical optics) depending on surface roughness. Finally, volume scattering from the snow cover as well as attenuation of surface scattering by snow cover are also investigated by a model. Results from those models are discussed with comparisons between ERS-1 and field 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".