Towards a TTOP ground temperature model for mountainous terrain in central‐eastern Norway
Bibliographic record
Abstract
Abstract The lack of simple mountain permafrost distribution models taking snow depth and site‐specific factors into consideration led us to test the regional Canadian temperature at the top of the permafrost or at the bottom of the seasonally frozen layer (TTOP)‐model in mountain terrain in central‐eastern Norway. The TTOP‐model uses seasonal n‐factors (nt and nf) and air temperature to model the mean annual ground‐surface temperature (MAGST), and a ratio of thawed to frozen thermal conductivity to model the average TTOP. This study presents 28 and 36 values of nt and nf, respectively. The potential incoming solar radiation, derived in a Geographical Information System (GIS), was used to parameterise nt, and average snow depth was used to parameterise nf. Due to limited information on the subsurface component of the model, only MAGST was modelled. The model was run for the 1961–90 normal period, the Little Ice Age and the year 2050. The model was evaluated against existing model predictions based on bottom temperature of winter snow (BTS) and geophysical soundings. Finally, critical values of snow depth, potential incoming solar radiation and thermal conductivity ratio that constrain negative MAGST and thus permafrost were determined. Copyright © 2007 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".