Bibliographic record
Abstract
Abstract Plug and Werner (Nature 2002, vol. 417, pp. 929–933) have constructed a model for the growth of ice‐wedge polygons. Contrary to field evidence, the model describes development of ice wedges in frozen ground beneath a centre line of ridges and assumes that effects associated with perturbations in the thermal stress field due to growth of individual wedges may dissipate in the long run. In the field, troughs, not ridges, overlie wedges, and development of the troughs as the ice wedges grow increases snow accumulation above the wedges, so that older wedges may crack less frequently than younger ice wedges. As a result, the ‘single‐ridge’ model does not replicate the inexorable evolution of polygonal networks in the field, because the underlying assumptions are inconsistent with field conditions. Instead, the ‘single‐ridge’ ice‐wedge model attributes network development only to periods of high thermal stress, i.e. particularly cold winters. The model has been used to simulate development of such networks ab initio, by considering conditions in a recently drained thaw lake. However, the results do not reproduce field conditions because the model operates in frozen, not freezing ground, and therefore the initial modelled network is greatly exaggerated from known field observations. While the conclusions of the modelling are presented in general terms, the model only considers epigenetic polygons, which comprise a small fraction of the ice‐wedge polygons in polar terrain. Other, more extensive, syngenetic and anti‐syngenetic (hillslope) polygon networks are not mentioned. Copyright © 2004 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".