Snow Accumulation in the Niaqunguk (Apex) River Watershed near Iqualuit, Nunavut, Canada
Bibliographic record
Abstract
Spring snowmelt is the largest input to Arctic hydrological systems. The spring snow distribution is extremely spatially variable and difficult to quantify. This study used field measurements and models to characterize and quantify the spring snow distribution in the 52 km2 Niaqunguk River watershed near Iqaluit, Nunavut. Three models were assessed for their ability to replicate spatial patterns and estimate total watershed snow storage. Two semi-distributed terrain-based models were calibrated, and a fully distributed process model, SnowModel, was run. All 3 successfully replicated spatial patterns and provided reasonable quantitative estimates, except for SnowModel's poor performance in 2015. SnowModel is useful for studying mid-winter processes, but requires user technical capacity and high-quality meteorological observations lacking for much of the Arctic. By comparison, the semi-distributed models provide an accurate estimate without high technical or meteorological data demands, and provide a framework to guide stratified snow surveying.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".