Bibliographic record
Abstract
Abstract The fundamental and dominant process operating in all ice‐wedge networks is thermal contraction fracturing. This assumption forms the basis of a numerical model combining fracture initiation and propagation in frozen ground and ice, influence of open fractures on stresses, growth of ice wedges and ground deformation above wedges (Plug and Werner, 2001 , 2002 ). Modelled polygonal networks self‐organise through interactions between fractures, stress and re‐fracture in ice wedges. The resultant polygonal form feeds back on fracturing in individual ice wedges. Spacing, wedge width and fracture frequency in wedges do not reflect mean climate parameters, but instead are sensitive to infrequent climate events and initial conditions, and may vary even under stationary climate — meaning that ice‐wedge casts are difficult to use as estimators of past climate. Burn ( 2004 ) suggested that that some of the assumptions underlying the model are incorrect in that they either misrepresent field conditions or ignore crucial site‐specific factors. These criticisms misread and invert the goal of our work, shared in part by any modelling exercise or field investigation, which is to elucidate common, robust behaviours and characteristics across a range of sites rather than to reproduce or describe in precise terms a particular instance. Copyright © 2007 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".