Micro‐Scale Frost Weathering of Sand‐Sized Quartz Grains
Bibliographic record
Abstract
Abstract The surface textures of almost 1300 quartz grains in the 0.8–1.0 mm and 0.5–0.8 mm size fractions were studied in order to analyse the effects of frost weathering. Some grains had undergone periglacial processes in present‐day active layers in Canada, Spitsbergen and Mongolia, whereas other grains were sampled from a former active layer in Poland. Microstructures were studied under a scanning electron microscope and the results statistically analysed in order to distinguish characteristic microstructures resulting from frost weathering. The quartz grains with these microstructures had been deposited by fluvial, aeolian and glacial processes and their characteristic microstructures are classified as primary microstructures. Microstructures that developed on primary ones as a result of frost weathering are called secondary microstructures. The most characteristic secondary frost weathering microstructures on the quartz grains are (1) small breakage blocks (<10 µm), (2) big breakage blocks (>10 µm) and (3) single small conchoidal fractures (<10 µm). These secondary microstructures developed commonly on the following primary microstructures: (1) sharp edges of big conchoidal fractures (>10 µm), (2) microsteps, (3) edge roundings and (4) crescentic gouges. These findings facilitate the reconstruction of cryogenic conditions, support the recognition of ancient active layers and indicate grains that are particularly susceptible to frost weathering. Copyright © 2015 John Wiley & Sons, Ltd.
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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".