HIGH ARCTIC PONDS, SOMERSET ISLAND, NUNAVUT: SPATIAL AND TEMPORAL VARIATION IN SNOWCOVER AND SNOWMELT
Bibliographic record
Abstract
ABSTRACT: Extensive low-gradient wetlands in the Canadian High Arctic have been the focus of recent studies. These wetlands typically are composed of a variety of wetland types; for example, wet meadow patches, ponds and riparian zones. Brown and Young’s (2004, submitted) study indicated that within one large wetland system near Creswell Bay (72 43'N, 94 15'W), a variety of ponds existed; some located in a moraine area, others in bedrock, while others could be classified as coastal and associated with isostatic rebound. The amount of snow together with the timing and duration of snowmelt are important factors in defining the amount of water available for ponds at the start of the summer season and also signaling the point where runoff and vertical water losses (e.g. seepage, evaporation) start to dominate. These processes can influence the sustainability of ponds or trigger their demise through desiccation. In 2004 detailed snow surveys and snow pits were conducted of selected ponds representing these distinct geomorphological areas. The snowmelt pattern was defined using a physically-based surface energy balance model. Snowcover and melt varied between pond types located in similar ground (e.g. moraine, bedrock) as well as contrasting landscape settings (e.g. moraine vs. coastal). An examination of mid- to late summer conditions of the ponds (e.g. water table, frost table) suggest that the survival of ponds depends on more than just an initial deep snowcover. o o
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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 | 0.001 | 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) | 0.002 | 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 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".