Snow-water-equivalent estimation using satellite data in forested and open areas in Ontario
Bibliographic record
Abstract
Passive microwave data from the Special Sensor Microwave/Imager (SSM/I) is being used to measure snow-water equivalent (SWE) in the prairie landscape. To extend this approach to Ontario, SSM/I data from three winters in Southern Ontario (non-forested) and Northern Ontario (forested) were compared to SWE modelled by ASAAM, a hydrological model. Algorithms combining channels of the SSM/I sensor were created and evaluated against modelled SWE. For the forested site, the brightness temperature difference between the vertical polarization of 19 GHz and 37 GHz channels was useful in interpreting SWE. At the non-forested site, addition of more channels improved the fit of the SSM/I-derived quantity to modeled SWE. The best results from the SSM/I-derived SWE were found for morning satellite overpasses when there was no liquid water present in the snowpack. Snow properties derived from SSM/I data, screened to remove no-snow and wet snow data, show promise for use in hydrological modelling in Ontario.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".