Estimating the snow water equivalent on the Gatineau catchment using hierarchical Bayesian modelling
Bibliographic record
Abstract
Abstract One of the most important parameters for spring runoff forecasting is the snow water equivalent on the watershed, often estimated by kriging using in situ measurements, and in some cases by remote sensing. It is known that kriging techniques provide little information on uncertainty, aside from the kriging variance. In this paper, two approaches using Bayesian hierarchical modelling are compared with ordinary kriging; Bayesian hierarchical modelling is a flexible and general statistical approach that uses observations and prior knowledge to make inferences on both unobserved data (snow water equivalent on the watershed where there is no measurements) and on the parameters (influence of the covariables, spatial interactions between the values of the process at various sites). The first approach models snow water equivalent as a Gaussian spatial process, for which the mean varies in space, and the other uses the theory of Markov random fields. Although kriging and the Bayesian models give similar point estimates, the latter provide more information on the distribution of the snow water equivalent. Furthermore, kriging may considerably underestimate interpolation error. Copyright © 2006 Environment Canada. Published by John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".