Snowcover and melt characteristics of upland/lowland terrain: Polar Bear Pass, Bathurst Island, Nunavut, Canada
Bibliographic record
Abstract
The seasonal snowcover and snowmelt (2008–2010) of an extensive low-gradient wetland at Polar Bear Pass, Bathurst Island, Nunavut, Canada (75°40′ N, 98°30′ W) was examined. This wildlife sanctuary is characterized by two large lakes and numerous tundra ponds, and is bordered by rolling hills with incised hillslope stream valleys. In arctic environments snow remains one of the most important sources of water for wetlands. End-of-winter snowcover measurements (snow depth, density, water equivalent) together with direct and modeled estimates of snowmelt provided an assessment of the seasonal snowcover regime of representative terrain types comprising upland (plateau, stream valley, late-lying snowbed) and lowland landscapes (wet meadow, ponds, lakes). In all three seasons, deep and persistent snowpacks occurred in sheltered areas (stream valleys) and in the lee of slopes adjacent to the wetland. Exposed areas yielded shallow snowpacks (e.g. plateau, pond) and they melted out rapidly in response to favorable weather conditions. Overall, the basin snowcover and melt progression was dominated by accumulation and melt occurring in upland areas. We surmise the sustainability of this low-gradient wetland is dependent on snowmelt contributions from upland sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".