Use of low-temperature scanning electron microscopy to compare and characterize three classes of snow cover
Bibliographic record
Abstract
This study, which uses low-temperature scanning electron microscopy (LTSEM), systematically sampled and characterized snow crystals that were collected from three unique classes of snow cover: prairie, taiga, and alpine. These classes, which were defined in previous field studies, result from exposure to unique climatic variables relating to wind, precipitation, and air temperature. Snow samples were taken at 10 cm depth intervals from the walls of freshly excavated snow pits. The depth of the snow pits for the prairie, taiga, and alpine covers were 28, 81, and 110 cm, respectively. Visual examination revealed that the prairie snow cover consisted of two distinct layers whereas the taiga and alpine covers had four distinct layers. Visual measurements were able to establish the range of crystal sizes that occurred in each layer, the temperature within the pit, and the snow density. The LTSEM observations revealed the detailed structures of the types of crystals that occurred in the snow covers, and documented the metamorphosis that transpired in the descending layers. Briefly, the top layers from two of the snow covers consisted of freshly fallen snow crystals that could be readily distinguished as plates and columns (prairie) or graupel (taiga). Alternatively, the top layer in the alpine cover consisted of older dendritic crystal fragments that had undergone early metamorphosis, that is, they had lost their sharp edges and had begun to show signs of joining or bonding with neighboring crystals. A unique layer, known as sun crust, was found in the prairie snow cover; however, successive samplings from all three snow covers showed similar stages of metamorphism that led to the formation of depth hoar crystals. These changes included the gradual development of large, three-dimensional crystals having clearly defined flat faces, sharp edges, internal depressions, and facets. The study, which indicates that LTSEM can be used to enhance visual data by systematically characterizing snow crystals that are collected at remote locations, is important for understanding the physics of snowpacks and the metamorphosis that leads to potential avalanche situations. In addition, the metamorphosis of snow crystals must be considered when microwave radiometry is used to estimate the snow water equivalent in the winter snowpack, because large snow crystals more effectively scatter passive microwave radiation than small crystals.
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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 | 0.000 |
| 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) | 0.000 | 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".