Adaptation of the SNOWMAP algorithm for snow mapping over eastern Canada using Landsat-TM imagery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
L'algorithme de cartographie de la neige SNOWMAP a été adapté aux données Landsat-TM et au contexte du territoire de l'Est du Canada. Six scènes de Landsat-5 TM ont été utilisées. Il a été constaté que la version originale de SNOWMAP sous-estime largement l'étendue du couvert nival. La modification de la version originale de l'algorithme en supprimant le seuil minimum de 0.1 sur la valeur de NDVI permet de combler ces lacunes. En outre, une procédure de correction spatiale appliquée aux résultats de l'algorithme SNOWMAP modifié permet d'améliorer la détection de la neige sous les forêts de conifères. En se basant sur un ensemble de données limité d'observations au sol (seulement les données de 40 sites étaient disponibles), la version modifiée de SNOWMAP semble plus performante dans la détection de la neige que la version originale. Un cas d'application est présenté afin de démontrer la pertinence d'utiliser les résultats de la version modifiée de SNOWMAP comme données de référence à haute résolution spatiale pour la validation des cartes de neige sur l'Est du Canada établies à partir des données historiques provenant de données satellitaires à moyenne résolution spatiale. \n \n<h2>Abstract</h2> \nThe snow mapping algorithm SNOWMAP was adapted to Landsat-TM data and to the context of eastern Canada. Six Landsat-5 TM scenes were used. It was found that the original version of SNOWMAP greatly underestimates snow cover extent. The modification made to the original algorithm, by cancelling the minimum threshold of 0.1 on the NDVI value, allows gaps to be filled in. In addition, a spatial correction procedure applied to the modified SNOWMAP algorithm results improves snow detection under coniferous forests. Based on a limited data set of ground-based observations (only 40 sites were available), the modified SNOWMAP seems to perform better in snow detection than the original version of the algorithm. An application case is presented in order to demonstrate the relevance of the modified SNOWMAP results as a high spatial-resolution reference for the validation of historical snow maps derived from medium spatial-resolution satellite data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 it