Climate and Permafrost 49 Identification of permafrost zones using selected permafrost landforms
Bibliographic record
Abstract
This paper examines the possibility of using the distribution of zonal permafrost landforms to aid in mapping permafrost distributions. In areas with under 50 cm snow cover in winter, permafrost zones can be defined by freezing and thawing indices. The relationship works for Norway, Spitz-bergen, Canada, and Mongolia. Since these include a wide range of thermal environments, it is pos-sible to trace the thermal ranges of various periglacial landforms. The zone of continuous permafrost markedly transgresses mean annual air temperature isotherms and is delimited by areas of Holocene felsenmeer and ice-wedge polygons in mineral soils. Active ice wedges in peats, earth hummocks, and cementery mounds, non-sorted polygons, and open system pin-gos extend into the zone of discontinuous permafrost. Sorted polygons and closed system pingos extend even further. Palsas, peat plateaux, and ice caves extend from the zone of continuous permafrost into that of sporadic permafrost in Norway and QuCbec. Cette Ctude examine la possibilite d'utiliser la rtpartition des formes de terrains lites au pergelisol zonal pour faciliter la cartographie de la rtpartition du pergtlisol. Dans les regions ou la couverture de neige est inftrieure a 50 cm en hiver, les zones de pergtlisol peuvent Ptre dkfinies a I'aide d'indices
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".