Assessment of Climate Change with Remote Sensing Data on Snow and Ice Cover in the Rocky Mountains Glaciers
Bibliographic record
Abstract
The effects are assessed of climate change on the temperature, snow cover and precipitation on the Wapta Icefield, located in the Rocky Mountains between Alberta and British Columbia in western Canada. Using remote sensing data and regression analyses, the study focuses on spatial changes of the snow cover area during the warm months of June, July, and August from 1990 to 2021. Landsat 5 and 8 satellite imagery are used to analyze environmental changes in the study region using Google Earth Engine (GEE) coding on the GEE platform. Normalized Difference Snow Index (NDSI), with a threshold of 0.4, and Normalized Difference Vegetation Index (NDVI) indices are used to detect snow-covered areas and identify vegetation areas, respectively. In addition, ERA5 and Global Precipitation Measurement (GPM) data are used to study trends in air temperature and precipitation changes. Examination of air temperature changes using ERA5 data from 1988 ~ 2019 shows an increase of 0.9 ℃ in average temperature for the area at the 80% significance level. The total precipitation in this region using (GPM) data from 2001 to 2021 shows a decrease in trends of precipitation. The results of the changes in snow cover in the warm months of the year within the period of 1990 to 2021 show a decrease of 45% with a significance level of 95%. Furthermore, the changes in the extent of vegetation during this same period show the extent of vegetation in the region has increased by 84% with a significance level of 95% and a −0.6 coefficient, indicating a relatively strong negative correlation between the snow cover and the vegetation cover, indicating an expansion of vegetation in the region with the continued loss of glacial ice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".