A nine-year, airborne laser scanning archive of glacier change, western Canada.
Bibliographic record
Abstract
Western Canada contains 72% of the glaciers within the Randolph Glacier Inventory (RGI) region 2 (Western Canada and USA), and these glaciers constitute 95% of the region’s total ice cover. Recent studies exploiting stereoscopic imagery from NASA’s Terra satellite (ASTER) have reduced biases in the number, type and distribution of glaciers used to assess regional glacier mass change. The elevation uncertainty of ASTER digital terrain models, in addition to infrequent sampling, confounds its use to detect trends in elevation change at seasonal scales or for small glaciers (< 1km2). Since summer 2014, the Hakai-UNBC Airborne Coastal Observatory (ACO) routinely acquires laser altimetric data at the end of the accumulation (late-April to early May) and ablation (early to late September) over many of western Canada’s glaciers and icefields using an aircraft equipped with an 1064-nm laser scanner and dedicated positional hardware. Post-processed uncertainties of repeated, co-registered elevational data over stable terrain yield uncertainties that are typically below ±0.3 m (±1s). Since 2020, our bi-annual acquisitions sample over 800 glaciers (about 2,000 km2), which constitutes about 15% of the total areal extent of ice in RGI-02. While the area-altitude distribution of ACO sampled glaciers largely accord with those of RGI-02, our sampling program captures fewer glaciers that exist at highest elevations, and the average glacier size surveyed by us is about three times larger than the average glacier size within RGI-02 (0.77 km2). Our archive reveals important aspects of glacier elevation change that cannot be obtained from existing publicly available sources of digital terrain data such as the magnitude of seasonal to inter-annual changes during the accumulation and ablation seasons, short-term horizontal transfers of mass, changes in volume and extent of transient late-lying snow, and the effects of short-term meteorological events (e.g. heat waves or forest fires) on regional melt events for a given year. We plan to release this archive to the public so it can be used to validate in-situ mass balance measurement programs, improve melt models and provide insight into physical factors that drive glacier change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".