INTERPRETING CHANGES IN ALBEDO AND MASS BALANCE AT WHITE GLACIER, CANADIAN ARCTIC ARCHIPELAGO
Bibliographic record
Abstract
Abstract. Alpine glaciers are sensitive to changes in land surface temperature (LST), and measurements of the mass balance are limited, especially for small glaciers. In this study, we investigate the relationship between snow albedo of the melting season (June, July, and August) and annual glacier mass balance of the White Glacier from 2002 to 2018. Since there are many gaps in the albedo data, we use a interpolation method to fill them and then obtain the average value of the melting season. The study results show that surface temperature plays a dominant role in albedo and mass balance changes, and mass balance change and albedo variation show a significant consistency, with an excellent correlation (R2 > 0.93). The acceleration of mass balance shows that the rate of mass reduction slows down, and the albedo change shows that the albedo increases from year to year. The interpolation albedo measurements using MODIS data can provide a useful means to reflect the annual change of glacier mass balance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".