Permafrost spatial and temporal variations near Schefferville, Nouveau-Québec
Bibliographic record
Abstract
Specially detailed studies of permafrost have developed at Schefferville because of the availability of long term data and the economic stimulus of the effect of permafrost on the iron mines. Knowledge of the three dimensional distribution of permafrost has been greatly expanded and energy budget studies have given confidence to many aspects of interpretation. Although the mean annual temperatures are -5 to -6.5°, large areas remain free of permafrost due to the winter insulation provided by deep snow which accumulates where snow drifting is subdued (e.g. in woodland). There are large year to year variations of frozen ground temperature in the upper 25 m (due to variations of snow conditions) and active layer depth (related to variations of summer weather conditions). Suprapermafrost groundwater movement is often concentrated along specific channels and transported heat causes very deep active layers (up to 12 m) or even maintains unfrozen zones (up to 30 m). On simple sites the active layer is primarily related to % vegetation cover, with mean depth ranging from 2.3 m under 100% vegetation to 3.6 m under bare ground. Considerable effort has been devoted to quantifying snow data for permafrost prediction and good results have been obtained from quanti-titavely relating ground temperatures to snow and a simulated groundwater measure. The permafrost is generally in balance with the present climate and new permafrost has developed in mine waste dumps.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".