The influence of surface characteristics, topography and continentality on mountain permafrost in British Columbia
Bibliographic record
Abstract
Abstract. Thermal and surface offsets describe mean annual ground temperature relative to mean annual air temperature, and for permafrost modelling they are often predicted as a function of surface characteristics and topography. As macroclimatic conditions influence the effectiveness of the underlying processes, knowledge of surface- and topography-specific offsets is not easily transferable between regions, limiting the applicability of empirical permafrost distribution models over areas with strong macroclimatic gradients. In this paper we describe surface and thermal offsets derived from distributed measurements at seven field sites in British Columbia. Key findings are (i) a surprisingly small variation of the surface offsets between different surface types; (ii) small thermal offsets at all sites (excluding wetlands and peat); (iii) a clear influence of the micro-topography at wind exposed sites (snow-cover erosion); (iv) a north–south difference of the surface offset of 4 °C in vertical bedrock and of 1.5–3 °C on open (no canopy) gentle slopes; (v) only small macroclimatic differences possibly caused by the inverse influence of snow cover and annual air temperature amplitude. These findings suggest that topoclimatic factors strongly influence the mountain permafrost distribution in British Columbia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".