Snow Mapping Coastal British Columbia - 2021 - Airborne Coastal Observatory
Bibliographic record
Abstract
Seasonal snow, like glaciers, provides thermal buffering to aquatic ecosystems in alpine and montane environments. The thermal properties of snow also provide a natural phase delay to rivers, supplementing flows during times when precipitation is at a minimum. The economic value of seasonal snow cover is difficult to quantify, but losses under a warming climate could exceed $500B (6). Seasonal snowmelt dominates the hydrology of large river basins in British Columbia (7, 8), yet direct observations of snow depth and snow water equivalent (SWE) in mountain basins are limited; these observations arise from sparse snow observation networks that tend to be centered around infrastructure and people. We thus have little knowledge of the spatial distribution of SWE (eg., SWE as a function of aspect and elevation). Seasonal snow also contributes to the glacier mass balance, so understanding its seasonal fluctuations will be important when assessing where and how quickly glaciers are shrinking. The lack of knowledge about SWE across large areas of the coast, severely limits our ability to calculate the contribution of seasonal snow (and ice) to the annual water budget of rivers, and thus makes it difficult to quantify how climate change will impact future water supply. These limited observations also contribute to a high degree of uncertainty in hydrological models used to predict the hydrological behavior of watersheds both in the past, present and future. In recent years, large scale surveys using airborne laser altimetry (LiDAR) has been applied successfully to watersheds in California (9), and this approach is only starting to be applied in British Columbia, due in large part to the fruitful collaboration between UNBC, Hakai and VIU. Hakai’s Airborne Coastal Observatory was developed to map and monitor icefields to oceans by using a combination of airborne Lidar (Light Detection and Ranging), high-resolution imagery, and hyperspectral imagery. Combined, the ACO sensors provide data to quantify changes in seasonal snow cover and glacier mass loss. The ACO is an aerial remote sensing platform used by the Hakai Institute to survey landscapes in detail. A Piper Navajo aircraft carries an array of integrated airborne mapping sensors installed to collect data in concert. The aircraft is operated and maintained by Kisik Aerial Surveys (Delta, BC).
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".