Urban Geocryology: Mapping Urban–Rural Contrasts in Active-Layer Thickness, Barrow Peninsula, Northern Alaska
Bibliographic record
Abstract
The maximum depth of seasonal thaw is a critical design factor for civil infrastructure in permafrost regions. Although maps of active-layer thickness (ALT) have been created for localized areas in undisturbed terrain, this has rarely been done within urbanized areas. The modified Berggren solution was used to map ALT at a resolution of 30 × 30 m over the 150-km2 Barrow Peninsula in northern Alaska. Emphasis was placed on analyzing differences in accuracy obtained in urbanized and relatively undisturbed tundra. Although the modified Berggren solution is known to provide more accurate estimates of frost and thaw depth than the Stefan solution, it has not been used previously in mapping applications. As part of the Barrow Urban Heat Island Study, seventy-one miniature data loggers were installed in and surrounding the City of Utqiaġvik (formerly Barrow) to measure air and soil temperature. The resulting data were used to calculate air and soil surface temperature fields, as well as summer n-factors, based on nine urban and rural land-cover classes. Regional soil and land-cover maps were used to obtain additional input data. Validation was performed by comparing probed ALT measurements with predicted pixel values. Model results confirm that the presence of urban infrastructure increases both the magnitude and the geographic variability of ALT relative to surrounding undisturbed tundra. The Berggren solution performed well for estimating mean values for land-cover classes in both rural and urban areas and has considerable potential as a tool for mapping ALT in other applications. Key Words: active layer, Alaska, Barrow, frozen ground, geocryology, mapping, permafrost, urban, Utqiaġvik.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".