Classification of glaciers during summer in Southern Rocky Mountain Trench using surface albedo and temperature anomalies
Bibliographic record
Abstract
Under most atmospheric conditions, the albedo and temperature of surface snow and ice are two of the main influences on the energy budget for glacier melting. Given that surface albedo and temperature are linked, knowing where and when negative albedo and positive surface temperature anomalies coincide is important for identifying locations and time periods in which anomalously high rates of surface melting are likely. We used measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's AQUA and TERRA satellites to map the albedo and surface temperature of snow and ice on glaciers in the of Southern Rocky Mountain Trench ecoregion in the summer months (June-August) from 2000 to 2018. We use these data to identify specific regions and time periods in which low albedo and high surface temperature coincide since these conditions are likely to support anomalously high rates of surface melting. We also use these data to identify regions/periods in which albedo is particularly low while surface temperature is average or low, since such conditions suggest localized and/or short-term decoupling between the two parameters. We found anomalously low albedo and average/low temperature consistently at multiple glaciers during time periods when there were major forest fire events. We suggest the low albedo results from deposition of pyrogenic carbon from forest fires. We found that, on average, ~25% of the glaciers in the region experienced increasingly negative albedo anomalies and increasingly positive temperature anomalies in summer months from 2000 to 2018. However, we also found that for ~45% of the glaciers that are small, there was a poor correlation between the timing of albedo and temperature anomalies. Our results indicate that the correlation between albedo and temperature was weaker for the small glaciers, and identify specific glaciers that are likely the most vulnerable to climate warming.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".