Interchangeability of mountain permafrost probability models, northwest Canada
Bibliographic record
Abstract
Abstract Spatial models of mountain permafrost probability based on measurements of the basal temperature of snow (BTS) and ground‐truthing were developed for three study areas located >200 km apart. The interchangeability of these locally derived empirical‐statistical models was examined by predicting BTS values from the equations developed at the other two sites, and using logistic regression to relate these modelled values to the local ground‐truthed data. Equations for two of the areas were effectively interchangeable, producing permafrost extent predictions within 2% of each other, and permafrost probabilities within ±0.1 for more than 85% of the cells. Predictions were much less similar when their equations were applied to the third area and when its equation was applied to them. Model interchangeability appears to depend on where a site lies on a continuum from elevation controlled (infinite ratio of the standardised coefficient of elevation to that of potential incoming solar radiation in the BTS equation), to radiation dominated (ratio much less than unity of the same variables). Extensive ground‐truthing is essential, helping to constrain the logistic regression and hence the overall permafrost percentage. Additional investigations are needed to relate these ratios to regional climate in order to allow the spatial interpolation of permafrost probability models across the North American Cordillera. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".