Geological and meteorological controls on icing (aufeis) dynamics (1985 to 2014) in subarctic Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Icings are widespread yet poorly understood winter hydrological phenomena that develop over the winter by freezing successive overflows of groundwater to the surface. Groundwater hydrology in arctic regions is constrained by geological setting and permafrost extent, and overflows are possibly driven by cold winters, winter warming intervals, high antecedent autumn rainfall, and low early winter snowfall. Consequently, icings are spatially recurrent but not necessarily annually nor to the same extent. We test the significance of identified meteorological forcing variables against a long‐term data set of icing dynamics and distribution we developed for the Great Slave region around Yellowknife, Northwest Territories. Climate is regionally consistent, but variable geology and permafrost create hydrological conditions representative of much of the subarctic. We mapped 5500 icings in the study area (21,887 km 2 ) with a semiautomated approach utilizing late spring Landsat archival images (1985 to 2014). Individual icing size, ranging 3 orders of magnitude (1.8 × 10 −3 km 2 to 4.1 km 2 ), is related to return frequency. Infrequent ice (25% return frequency) accounts for 94% of the total icing area (86 km 2 ). Winter warming intervals (≥5°C; typically over 1–3 days) and autumn rainfall (September and October) explain 28% of icing density interannual variation overall. Interannual icing variation and significant meteorological forcing variables differ among ecoregions where varied geological settings and permafrost conditions influence the hydrological regime. Future icings may develop less frequently due to decreasing winter warming intervals, but increasing autumn rainfall may increase icing density where Canadian Shield leads to strong threshold‐mediated runoff generation processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it