Influence of recent warming and ice dynamics on glacier surface elevations in the Canadian High Arctic, 1995–2014
Bibliographic record
Abstract
ABSTRACT Repeat airborne laser altimetry measurements show widespread thinning (surface lowering) of glaciers in Canada's Queen Elizabeth Islands since 1995. Thinning rates averaged for 50 m elevation bins, were more than three times higher during the period 2005/06 to 2012/14 pentad than during the previous two pentads. Strongly negative thickness change (dh/dt) anomalies from 2005/06 to 2012/14, relative to the 1995–2012/14 mean, suggest that most of the measured thinning occurred during the most recent 5–6 year period when mean summer land surface temperatures (LSTs) were anomalously high and the mean summer black-sky shortwave broadband albedos (BSA) were anomalously low, relative to the 2000/01–15/16 period, and upper-air (700 hPa) and near surface (2 m) air temperatures were between 0.8°C and 1.5°C higher than 1995–2012 mean. Comparisons of dh/dt with mean summer LST and BSA measurements from the Moderate Resolution Imaging Spectroradiometer and with surface longitudinal strain rates computed from surface velocity fields derived from RADARSAT 1/2 and Landat-7 ETM + data suggest that surface elevation changes were driven mainly by changes in climate. An exception to this occurs along many fast-flowing outlet glaciers where ice dynamics appear also to have played an important role in surface elevation changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".