Microphysique du manteau neigeux : évolution de la surface spécifique de la neige dans les Alpes et l'Antarctique ; impact sur la chimie atmosphèrique
Bibliographic record
Abstract
Snow covers up to 50% of land masses in the northern hemisphere in winter and its potential for interaction with the atmosphere has been demonstrated by studies in Polar Regions. This can proceed by complex processes that include heterogeneous reactions on the snow surface, adsorption/ desorption of gases, sublimation of snow and its solutes and co-condensation of water vapor and other gases. Understanding and quantifying theses processes requires the knowledge of physical parameters among which the specifie surface area (SSA) of snow. It represents the surface area accessible to gases per mass unit. ln spite of the Importance of this parameter, few data have been obtalned earlier, which led us to perform this study on the SSA of snow and its evolution ln the snowpack. SSA was measured by methane adsorption at liquid nltrogen temperature (77K). In order to understand processes involved ln SSA evolution, photomacrographs and plctures obtained by scanning electronic microscopy were used. SSA values obtained were in the range 1540 to 400 cm2/g for fresh snow. Values decrease down to 100 cm2/g for aged snow. This decrease results from morphological changes associated to snow metamorphism. Temperature and wind are the main factors which drive the kinetics of SSA decrease. At AJert (Canadian Arctic), a detailed study of the microphysics of the snowpack allowed the measurement of the uptake capaclty of adsorbable trace gases by the snowpack. The total surface area of the snowpack ranged from 1160 to 3710 m2/m2. Therefore, we demonstrated that snowpack may sequester most of the species in the (snow + boundary layer) system. SSA values were also used to determinate incorporation pro cesses of formaldehyde in snow.
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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 | 0.001 |
| 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".