Bibliographic record
Abstract
Ice mechanical behavior is time-dependent, as has been known for many decades. But in many references, the attempt is made to use time-independent plasticity theory. The relevant analytical approach that accounts for time is viscoelastic theory. The need for this approach is made quite essential by study of microstructural changes that occur in ice under high stresses. In no case does there appear to be a clear yield condition, with flow occurring after a certain threshold value. Furthermore, the microstructural changes occurring under stress result in a highly significant enhancement of the creep rates. This results in a spatially varying viscoelastic response that is a function of prior stress history. The ice response is then a function of position resulting in a microstucturally modified layer in the region where compressive stress is applied. This can be deep or highly localized, depending on the loading rate. The most promising approach is that based on damage mechanics combined with viscoelasticity, using the thermodynamics of irreversible processes. Ice is also prone to fracture, especially at high loading rates and under high stresses. This is basic to the notion of scale effect. Fracture processes are also time-dependent in viscoelastic materials, a phenomenon that needs to be explored further. Furthermore, failure often will take place in a random fashion, depending on the distribution of flaws in ice. This indicates strongly that a theory based on “weakest-link” hypotheses and probability theory is appropriate. Finally, some aspects relevant to practical data analysis are discussed. These include measurement uncertainties of Molikpaq data, and geometric approximations of ice features, e.g. ridges as uniform beams.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".