Bibliographic record
Abstract
Abstract Satellite and conventional snow water equivalent (SWE) dataseis reveal a well‐defined zone of high winter season SWE (>100 mm) that extends across the northern boreal forest of Canada. SWE coefficient of variation (CV) patterns derived from a monthly averaged (1978–2002) passive microwave derived time series show a high degree of interannual variability across open prairie, southern boreal, and open tundra regions of North America while SWE across the northern boreal forest was highly invariant. The potential existence of a consistent SWE zone resistant to interannual climatic variability over the past 25 years is intriguing in the context of the sensitivity of snow cover to climate variability and change. A ground sampling campaign conceived specifically to evaluate SWE distribution across the northern boreal forest was conducted in northern Manitoba during the 2003–04 winter season. Data from this survey confirmed the SWE gradient across the boreal forest, although satellite‐derived retrievals for the tundra were consistently low. A series of Canadian Regional Climate Model (CRCM) simulations were conducted to identify feedbacks between the atmosphere and land surface for a domain focused on the northern boreal forest. A control simulation produced monthly patterns of SWE distribution that closely matched the passive microwave retrievals. Water budget computations showed the SWE accumulation pattern to be a function of the modelled regional precipitation pattern, and not the result of surface processes such as melt or evaporation/sublimation. Mean monthly patterns of 850‐hPa fronto genesis forcing corresponded closely to the patterns of accumulated SWE suggesting that lower tropospheric frontal activity was responsible for the snowfall events that led directly to the deposition of the northern boreal SWE band. CRCM sensitivity experiments were conducted with perturbed land cover and terrain. Only subtle differences in SWE accumulation and frontogenesis patterns relative to the control run were found when complete grassland cover was prescribed, though removing orography greatly enhanced the magnitude and zonal extent of the SWE band.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".