On the Sensitivity of the Normalized Difference Snow Index to Metamorphic Changes Elicited in Dry and Wet Snowpacks
Bibliographic record
Abstract
Alterations in seasonal snow covers can have profound effects not only on the planet’s climate, biodiversity and fresh water supplies, but also on the occurrence of natural hazards like floods and avalanches. The mapping and monitoring of these alterations often rely on the calculation of spectral in-dices such as the NDSI (normalized difference snow index). Despite the extensive use of the NDSI in remote sensing applications, several aspects related to its sensitivity to changes in snow characteristics remain to be broadly unveiled. These changes can take place during environmentally-induced metamorphic processes, which are being accentuated by increasing global warming conditions. In this work, we systematically examine the impact that concomitant metamorphic changes on key nivological characteristics, namely grain size and density, can have on the NDSI of snowpacks with varying liquid water contents and irradiated from distinct light incidence directions. Our investigation is carried out through controlled in silico experiments conducted using a first-principles simulation framework supported by in situ measured data obtained from natural snowpacks.
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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 | 0.002 |
| 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 | 0.000 |
| 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".