Multi-scale analysis of the spatial variability of the snow water equivalent (SWE) over Eastern Canada.
Bibliographic record
Abstract
Snow cover is a key factor in the climate system and the hydrologic cycle in Eastern Canada (Quebec and \nLabrador). Snow survey network still the main source of data on snow in this vast territory. However, data from \nstations are only representative of local phenomena. In addition, the density and spatial distribution of the network \nare not optimal. Therefore, in its current configuration, the network offers a fragmentary view of the phenomenon \nand does not adequately represent its spatial variability at the regional scale. Indeed, the characteristics of the \nspatial variability of snow cover (spatial scales, spatial structures and spatial discontinuities) are often non-linear \nand complex to model. This is an important source of error in spatialisation of physical parameters of snow cover \n(density, thickness and snow water equivalent). It is therefore fundamental to a better estimation, integrating the \ncharacteristics of the spatial variability in spatial modelling of snow physical parameters. Moreover, due to the \nfragmentary knowledge of the phenomenon, it is recommended to adopt a functional approach that integrates the \nunderlying processes that control its spatial variability. Indeed, the spatial variability of snow cover is under the \ninfluence of environmental factors (local and regional). The latter, commonly available in all parts of the territory, \nare responsible for the underlying processes that generate spatial structures. They are thus responsible for the existence \nof homogeneous spatial units forming a strong contrast with the spatial structures surrounding areas. The \nmain objective of this study is to analyze the multi-scale spatial variability of SWE. First, the spatial variability of \nSWE compared to regional environmental factors (latitude, longitude, altitude and distance to the ocean) and local \n(slope, curvature slopes, solar radiation, orientation, etc.) was analyzed. Local indices to characterize different spatial \nstructures were also calculated. Subsequently, the geographical areas with homogeneous spatial structures were \ndelineated using a segmentation approach multi-spatial resolutions, integrating the weight of explanatory factors. \nThe weight factors were determined by multivariate statistical analysis. The results of segmentation were validated \nusing nonparametric statistical test (Kruskal-Wallis) applied to the data of the EEN of each pair of adjacent geographic \nareas. At the regional level, spatial segmentation has identified six geographic zones distinguished by the \ndisposition of large relief. At the local level, spatial segmentation has highlighted the role of physiographic factors \nin the spatial variability of snow cover (slope, curvature and occupation of land).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".