Snow cover and snowmelt of an extensive High Arctic wetland: spatial and temporal seasonal patterns
Bibliographic record
Abstract
This study examined the end-of-winter snow storage, its distribution and the spatial and temporal melt patterns of a large, low gradient wetland at Polar Bear Pass, Bathurst Island, Nunavut, Canada. The project utilized a combination of field observations and a physically-based snowmelt model. Topography and wind were the major controls on snow distribution in the region, and snow was routinely scoured from the hilltop regions and deposited into hillslopes and valleys. Timing and duration of snowmelt at Polar Bear Pass were similar in 2008 and 2009. The snowmelt was initiated by an increase in air temperature and net radiation receipt. Inter-annual variability in spatial snowmelt patterns was evident at Polar Bear Pass and was attributed to a non-uniform snow cover distribution and local microclimate conditions. In situ field studies and modelling remain important in High Arctic regions for assessing wetland water budgets and runoff, in addition to model parameterization and validation of satellite imagery. Editor Z.W. Kundzewicz Citation Assini, J. and Young, K.L., 2012. Snow cover and snowmelt of an extensive High Arctic wetland: spatial and temporal seasonal patterns. Hydrological Sciences Journal, 57 (4), 738–755.
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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 | 0.000 | 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) | 0.001 | 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".