VALIDATION OF A METHOD FOR SNOW COVER EXTENT MONITORING OVER QUEBEC (CANADA) USING NOAA-AVHRR DATA
Bibliographic record
Abstract
slivitz(at)cite.net This work comes within the scope of a multidisciplinary study aimed at validating the hydrological simulations of the Canadian regional climate model over Quebec (Canada). Snow cover is a key factor in the modelling process. Because of their low density, conventional local observation net-works do not provide enough accurate data to map snow cover on a large scale and with an ade-quate spatial resolution for regional climate modelling. Alternatively, this is easily feasible using visible and infrared satellite imagery. However, available satellite snow cover products are unus-able for our special needs, because they either have an inadequate spatial resolution or too short observation series. The objective of this study was therefore to develop an automatic algorithm for snow cover extent mapping using data from the AVHRR sensor on board NOAA satellite series, which allows monitor-ing the space-time evolution of snow cover extent over a long period of time and with a “fine ” spa-tial resolution (1x1 km2). Snow cover extent mapping results were validated against in situ snow occurrence observations. The algorithm was tested over the province of Quebec (Canada) for three specific periods: 1998-1999, 1991-1992 and 1986-1987. The algorithm identifies surface class (snow/no-snow) with an average total success rate of 87%. The algorithm performances were higher in snow detection (90%) than they were for no-snow surfaces (82%).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".