Glacier and Ice Aerial Surveys in British Columbia - 2023-2024 - Hakai Airborne Coastal Observatory
Bibliographic record
Abstract
The Airborne Coastal Observatory (ACO), developed by the Hakai Institute, is an aerial remote sensing platform designed to map landscapes from icefields to oceans. Using a Piper Navajo aircraft operated by Kisik Aerial Surveys (Delta, BC), the ACO integrates LiDAR (Light Detection and Ranging), high-resolution imagery, and hyperspectral sensors to collect detailed environmental data in a single pass. These tools are used to monitor changes in regional glaciers and snowpack, supported by field observations and validated through ground-based sampling and sensor networks. A primary focus of the ACO is to improve understanding of snow and glacier dynamics across BC’s mountainous coast. Long-term monitoring by the Hakai ACO captures variations in snow inputs, which differ dramatically between rain-dominated and snow-dominated zones. Snowpack is a critical factor in watershed modeling, helping to explain variations in runoff and providing insights into how watersheds respond to changing climate conditions. Coastal elevation gradients offer some resilience in high-altitude watersheds, but the region remains particularly vulnerable due to its comparatively mild winters. For more information on post processing, data quality assurance, software used, and summary of results please contact data@hakai.org
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".