Predicting Snow Depth in a Forest–Tundra Landscape using a Conceptual Model Allowing for Snow Redistribution and Constrained by Observations from a Digital Camera
Bibliographic record
Abstract
Estimation of snow depth in the forest–tundra landscape remains a challenge because of a lack of reliable and frequent observations on precipitation and snow depth. Snow models forced by gridded meteorological datasets are often the only option available for assessing snow depth at the local scale. Unfortunately, these models generally do not take into account the snow redistribution process between open and forested areas which frequently occurs in the forest–tundra landscape. A simple modification to an existing snow accumulation and melt model is proposed in order to allow for snow redistribution. Along with a technique for taking advantage of snow depth observations obtained from a digital camera, the model is shown to provide accurate predictions of snow depth at the local scale when forced with precipitation data from Environment Canada's Canadian Precipitation Analysis. Results from this study suggest that instrumenting automated weather stations with a digital camera, together with small modifications to an existing model used operationally for snow depth prediction, could result in significant improvements to snow depth prediction and analysis in this environment. Further testing at sites where snow water equivalent of the snowpack is available should, however, be performed to fully validate the method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".