Interactive comment on “Metamorphism of Arctic marine snow during the melt season. Impact on albedo” by Gauthier Verin et al.
Bibliographic record
Abstract
This paper presents results of snow physical properties and spectral albedo measured with high frequency on sea ice in Baffin Bay of Northern Canada during two melt seasons in 2015 and 2016, and result of spectral albedo simulations using radiative transfer model.The authors discuss in detail the evolutions of snow physical and optical properties by dividing the observation period of each year into 4 phases in terms of snow stratigraphy, snow specific surface area (SSA), snow density, and spectral albedos.They also discuss causes of temporal variation of albedos in the visible (500 nm) and near infrared (1000 nm) by comparing the albedos measured with those calculated using radiative transfer model, and by sensitivity tests for effects of SSA and snow layer thickness on albedos.My overall impression for the manuscript is that the first half part (∼Section 3.3) is
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.080 | 0.027 |
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".