Geomorphologica and climatic factors influencing morphological features of ice wedge polygons in arctic zone
Bibliographic record
Abstract
Location and climate factors governing morphological features of ice wedge polygons in arctic zone Abstract This thesis reports the geometry of active soil and ice-wedge polygonal network located along the Canadian Arctic and in Alaska. This High Arctic periglacial environment was chosen to ensure active thermal-contraction cracking of permafrost in Holocene allowing comparison of observed ice-wedge polygons with existing climatic data. Geoinformatic software (Google Earth, ArcGIS) was used to obtain and digitalize satellite images of polygonal networks located around Eureka, Mould Bay, Churchill, Rankin Inlet, Inuvik, and Kotzebue. Defined polygonal networks were statistically analyzed in order to define predominant environmental factors controlling morphological parameters of these polygonal networks. Polygon size (overall influence computed on the basis of coefficient of determination) is determined mostly by the duration of development (18 %), frequency of cyclonic passage (17.8 %) and winter air temperature (16.1 %). Conversely, polygon regularity results mainly from nature of the substrate (21.8 %), winter wind speed (15.1 %) and snow cover thickness (12.2 %). Dominant polygon elongation in the polygonal network follows closely the winter wind direction (3/4 of all networks) as the result of snow...
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".