Microstructure mapping: a new method for imaging deformation-induced microstructural features of ice on the grain scale
Bibliographic record
Abstract
Abstract This work presents a method of mapping deformation-related sublimation patterns, formed on the surface of ice specimens, at microscopic resolution (3–4 μm pixel −1 ). The method is based on the systematic sublimation of a microtomed piece of ice, prepared either as a thick or a thin section. The mapping system consists of an optical microscope, a CCD video camera and a computer-controlled xy -stage. About 1500 images are needed to build a high-resolution mosaic map of a 4.5 × 9 cm section. Mosaics and single images are used to derive a variety of statistical data about air inclusions (air bubbles and air clathrate hydrates), texture (grain size, shape and orientation) and deformation-related features (subgrain boundaries, slip bands, subgrain islands and loops, pinned and bulged grain boundaries). The most common sublimation patterns are described, and their relevance for the deformation of polar ice is briefly discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".