Formation and strengthening of layers of dry faceted crystals above artificial melt–freeze crusts from overburden stress in a controlled environment
Bibliographic record
Abstract
Layers of faceted crystals were grown from naturally fallen snow above wet snow layers in a temperature-controlled laboratory. Static loads were then applied to the weak layers to represent overburden snow. Eleven experiments with constant loads were analyzed, equivalent to 260 to 1500 Pa of overburden stress. The density of the slab above the weak layer increased with time, following a power law relationship with an average exponent of 0.10. Shear strength of the weak layer increased with time for all experiments, also following a power law relationship. Early-time rates of strength gain averaged 250 Pa·day−1 for the constant-load experiments over the first 3–5 days, decreasing to an average of <1 Pa·day−1 after approximately 30 days. Exponents for the power law relationships ranged between 0.09 and 0.35 with an average of 0.26 ± 0.08. Sintering was likely the dominant process for strength gain, although densification probably contributed as well. Three experiments were conducted in which the overburden was increased in stages; these exhibited an average strength gain rate of 270 Pa·day−1 over 4–8 days with approximately linear relationships, highlighting the importance of cumulative snowfall for layer strength gain.
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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 | 0.000 | 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".