Metamorphism of Arctic marine snow during the melt season.Impact on albedo
Bibliographic record
Abstract
Abstract. The energy budget of Arctic sea ice is strongly affected by the snow cover. Intensive sampling of snow properties was conducted near Qikiqtarjuak in Baffin Bay on typical landfast sea ice during two melt seasons in 2015 and 2016. The sampling included stratigraphy, vertical profiles of snow specific surface area (SSA), density and surface spectral albedo. Both seasons feature four main phases: I) dry snow cover, II) surface melting, III) ripe snowpack and IV) melt pond formation. Each of them was characterized by distinctive physical and optical properties. Highest SSA of 49.3 m2 kg−1 was measured during phase I on surface windslab together with a high broadband albedo of 0.87. The next phase was marked by alternative episodes of surface melting which dramatically decreased the SSA below 3 m2 kg−1 and episodes of snowfall reestablishing the pre-melt conditions. Albedo was highly time variable especially in the near-infrared with minimum values around 0.45 at 1000 nm. At some point, the melt progressed leading to a fully ripe snowpack composed of clustered rounded grains in phase III. Albedo began to decrease in the visible as snow thickness decreased but remained steady at longer wavelengths. Moreover, its spatial variability clearly appeared for the first time following snow depth heterogeneity. The impacts on albedo of both snow SSA and thickness were quantitatively investigated using a radiative transfer model. Comparisons between albedo measurements and simulations show that our data on snow physical properties are relevant for radiative transfer modeling. They also point out to the importance of the properties of the very surface snow layer for albedo computation, especially during phase II when several distinctive layers of snow superimposed following snowfalls, melt or diurnal cycles.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".