Geometric Evolution of Polygonal Terrain Networks in the Canadian High Arctic: Evidence of Increasing Regularity over Time
Bibliographic record
Abstract
ABSTRACT Polygon networks are usually described qualitatively as becoming more regular through time, but such a concept has yet to be demonstrated numerically. The aim of this study is to address this question quantitatively in order to determine if polygonal terrain networks actually become more regular as they develop. Spatial point pattern analysis (SPPA), which can quantify overall network geometries based on the randomness or regularity exhibited by the spatial arrangement of polygon‐bounding trough intersections, was used at three ice‐wedge polygon sites in the Canadian High Arctic. SPPA was applied in two ways: (i) on the present‐day networks observed in the field; and (ii) on historical arrangements derived by distinguishing primary from secondary troughs. In all cases, the polygonal networks had undergone a statistically significant regularisation over the course of their development. Although the method was applied only to terrestrial ice‐wedge polygons, such an approach may also be useful for interpreting the evolution of Antarctic sublimation polygons and geometrically similar polygonal networks on Mars. Copyright © 2012 John Wiley & Sons, Ltd. and Her Majesty the Queen in Right of Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".