IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
Bibliographic record
Abstract
Satellite derived snow cover area (SCA) is a critical parameter in snowmelt modelling, and is used in numerous hydrological and climatological studies. A major limitation of current SCA modelling when using optical satellite sensors is mapping snow in forested areas. The most ideal case for mapping snow in dense forested areas is to have landcover data indicating the location of forested regions and then use separate classification criteria for forested and non-forested areas. This study investigates the MODIS (Moderate Resolution Imaging Spectroradiometer) snow-mapping algorithm “Snowmap ” and its ability to map snow in the Northern Boreal Forest of Manitoba. Landcover data enhanced snow-mapping algorithms were developed and compared with MODIS snow products during the snowmelt period of 2001 and 2002. The use of Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) values in the MODIS Snowmap algorithm to detect snow in forested areas was proven successful in this study. Landcover based algorithms and the MODIS algorithm both mapped similar amounts of SCA during the melt period in each study year. The
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".