Bibliographic record
Abstract
The glaciers of the Canadian Arctic are currently the third largest contributor to global sea level rise due to enhanced Arctic warming driving increased glacier melt and runoff (Zemp et al., 2019). Recent modelling suggests that the glaciers of the southern Qikiqtaaluk region, specifically Baffin and Bylot Islands, are 70% more sensitive to 1°C of warming (Brice et al., 2018). However, these studies lack field-based data to contextualize the drivers of glacier change in this region. Light absorbing particles (LAPs) such as dust, ash, and algae lower the albedo of snow and ice, which is the fraction of light a surface reflects, leading to an increase in the absorption of incoming radiation (Aubry-Wake et al., 2022). On glaciers in western Canada (Engstrom et al., 2022), the European Alps (Di Mauro et al., 2020) and south-western Greenland (Cook et al., 2020), positive feedback mechanisms have been discovered where summer snow and ice melt, augmented by LAPs, leads to the localized release of nutrients in ice and the subsequent enhanced growth of algae. Preliminary remote sensing analysis revealing a dark purple hue on glaciers in the Akshayuk Pass region of Auyuittuq National Park in Baffin Island, NU indicate these processes are likely occurring. The question of how this phenomenon is impacting glacier albedo, and therefore glacier melt which has implications for mass balance and regional sea level rise in the Canadian High Arctic has yet to be answered. Field-based sampling of LAPs on Turner Glacier, characterized through light microscopy and scanning electron microscopy, combined with in-situ hyperspectral spectroradiometer measurements (320nm-1100nm) of the sampled LAPs, and fine resolution (1cm) UAV surveys of sampling sites, are integral to develop a field-calibrated remote sensing approach to monitor LAP accumulation on the glacier surface and to assess the associated melt potential.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".