Bibliographic record
Abstract
Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing km-scale areal averages over shallow snowcovers. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada with validation data collected from manual snow depth and density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 m s-1, altitude of 15 m, and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 m s-1, altitude of 8 m, and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the results substantially and permitted estimation of SWE at a spatial resolution of greater than 0.25 m with a ±14.3 mm SWE error relative to manual snow survey density and UAV-lidar based depths to estimate SWE. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide high resolution, high accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.026 | 0.023 |
| Insufficient payload (model declined to judge) | 0.189 | 0.143 |
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".