The utility of computer-processed NOAÂ imagery for snow cover mapping and streamflow simulation in Alberta
Bibliographic record
Abstract
ABSTRACT Computer processed NOAA satel-lite imagery for a large (2210 km^) moun-tainous basin of Alberta was found to be useful for snowmelt modelling using SSARR (Streamflow Synthesis and Reservoir Regula-tion) in 1985 and 1986. The % SCA (snow-covered area) versus % SWE (snow water equi-valent) relationship used in the snowmelt routine of the model is defined by forcing the model to use NOAA SCA estimates. The % SCA versus % SWE curve is markedly diffe-rent in the two years modelled, and results suggest that a family of curves with initial SWE as the third parameter will improve simulation results. SCA statistics were generated for a series of cloud-free dates in each year by a one-dimensional threshold-ing of channel 2 (near IR) data. A technique for correcting the illumination of images with a lower solar angle is presented. L'utilité de l'imagerie NOAA traitée par ordinateur pour la cartographie de la neige et la simulation de l'écoulement en Alberta RESUME Les images digitales de satel-lites de NOAA se sont avéré utiles dans la modélisation la fonte des neiges en 1985 et 1986 d'un grand (2210 kmr) bassin monta-gneux de 1'Alberta au moyen du simulateur SSARR. La courbe du % ECN (l'étendue du couvert neigeux) versus le % EAN (équivalent en eau de la neige) utilisée dans la sous-routine de fonte du simulateur est définie en forçant le modèle SSARR à utiliser les estimés ECN de NOAA. Le fait que les courbes du % ECN versus % EAN générées pour les deux années de données sont différentes suggère que les simulations seront améliorées par
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".