Method for measurement of snow depth using time-lapse photography
Bibliographic record
Abstract
Snow depth (SD) is an essential climate variable widely used for flood forecasting, water quantity assessments, road and building safety assessment, habitat assessment, and climate studies. Currently, SD is systematically monitored using manual ruler measurements or dedicated instrumentation with a limited spatial footprint (~1m2). Here, an approach for automated SD estimation using images of narrow stakes remotely acquired by a 3Mpixel trail camera is described. The approach relies on the automated application of image processing and machine learning algorithms packaged within a single freely available application deployed on a personal computer or cloud computing environment. The application requires minimal user input to define an initial template image and provides two independent estimates of SD at each stake that can be used to produce an estimate of total uncertainty and sources of error. The system is compared to both manual ruler and ultrasonic instrument SD estimates at an open site and in a deciduous forest. Initial results over a melt period indicate the automated method agreed to within 1cm (RMSE) of manual ruler estimates and to within 3.6cm of ultrasonic estimates; with the latter comparison including spatial variability between measurement locations. The automated system should be considered for further deployment and evaluation over a range of surface and climate conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".