Swings in runoff at Polar Bear Pass: an extensive low-gradient wetland, Bathurst Island, Canada
Bibliographic record
Abstract
Abstract Hydrologic studies in northern landscapes indicate there is a critical need to explore how arctic stream discharge patterns and water budgets may be shifting in response to climate warming. The focus of this study was to: (1) assess the pattern of runoff out of Polar Bear Pass, a low-gradient watershed (75°40′N, 98°30′W), during two contrasting spring/summer seasons: 2012 (warm, early melt) versus 2013 (cool, late melt); (2) quantify the seasonal water budgets; and (3) place these results in the context of other arctic basin studies. The end-of-winter snowpack was quantified using a terrain-based approach. A physically based snowmelt model using local weather station data provided daily melt estimates. Streamflow at the eastern outlet was estimated using the mid-section velocity approach. Snow water equivalent (SWE) was higher in 2013 while snowmelt began and ended earlier in 2012. Stream hydrographs showed a rapid rise in flow driven by meltwater from the northern part of the Pass in 2012. This was followed by a series of secondary peaks, melt contributions from the southern end. In 2013, the largest runoff peaks came from the southern sector. Runoff ratios and water budgets varied between the two years, and runoff in 2013 was similar to High Arctic watersheds in the early 1970s.
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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 | 0.002 | 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".