KAN- and CNN-Driven Snow Stratigraphy Retrieval Across Antarctic and Subarctic Environments
Bibliographic record
Abstract
Accurate retrieval of snow density (ρ)and specific surface area (SSA) is critical for understanding snowpack evolution, energy balance, and hydrological processes. This study leverages the Kolmogorov–Arnold Network (KAN) relative to a conventional Convolutional Neural Network (CNN) in predicting vertical profiles ofρand SSA across two contrasting environments: the stable, cold Antarctic plateau and the heterogeneous Canadian subarctic zones. Results demonstrate that both models accurately reproduce large-scale stratigraphic patterns, but KAN generally achieves higher depth-preserving skill for SSA in the more complex Canadian environment, while CNN yields more accurateρretrieval across two regions. Depth-resolved correlation patterns reveal performance fluctuations in mid-layers, attributed to compaction, vapor transport, and metamorphic processes not explicitly represented in purely data-driven models. SHAP analysis confirms the complementary sensitivity of low- (10 GHz, 19 GHz) and high-frequency (37 GHz, 89 GHz) microwave channels to deeper and near-surface snow layers, respectively. Under extreme snow conditions (highρor SSA), both models capture strong physical correlations between snow properties, temperature, measurement depth, and microwave signals, while also revealing saturation effects at high density and low SSA. The results highlight KAN's potential as a robust alternative to CNN for cryospheric remote sensing, especially in heterogeneous snow regimes where nonlinear feature interactions and depth-dependent relationships are critical. The demonstrated capability has implications for satellite retrieval algorithm design, avalanche hazard forecasting, and climate model parameterization of snow–atmosphere interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".