Characterization of Canadian High Arctic glacier surface albedo from MODIS C6 data, 2001–2016
Bibliographic record
Abstract
Abstract. Inter-annual variations and longer-term trends in the annual mass balance of glaciers in Canada's Queen Elizabeth Islands (QEI) are largely attributable to changes in summer melt. The largest source of melt energy in the QEI in summer is net shortwave radiation, which is modulated by changes in glacier surface albedo. We used measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors to investigate large scale spatial patterns and temporal trends and variability in the summer surface albedo of QEI glaciers and their relationship to observed changes in glacier surface temperature from 2001 to 2016. Mean summer black-sky shortwave broadband albedo (BSA) decreased at a rate of 0.029 ± 0.025 decade-1 over that period. Larger reductions in BSA occurred in July (−0.050 ± 0.031 decade-1). No change in BSA was observed in either June or August. Most of the decrease in BSA, which was greatest at lower elevations around the margins of the ice masses, occurred between 2007 and 2012 when mean summer BSA was anomalously low. The First Principal Component of the 16-year record of mean summer BSA was well correlated with the mean summer North Atlantic Oscillation Index, except in 2006, 2010, and 2016. During this 16-year period, the mean summer LST increased by 0.046 ± 0.036 °C yr-1 and the BSA record was negatively correlated (−0.64, p
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".