Spatial and temporal trends of snow cover properties in a large subarctic basin: implications for basin-wide, end-of-winter snow water equivalent estimates
Bibliographic record
Abstract
Basin-wide snow water equivalent (SWE) is an important hydrologic variable.For large basins, SWE is often estimated using a sparse network of sites.In this study, historical snow surveys conducted across the ~13,700 km 2 Snare River basin near Yellowknife, NWT, were analyzed to identify local and regional scales of variability as well as temporal trends.Two field seasons of enhanced surveys (2016/17) were conducted.Snow regimes were found to differ significantly between sites north and south of treeline.No statistically significant temporal trends in SWE were detected but snow depth was found to be increasing while snow density was decreasing.Surveys on lakes showed consistently lower SWE than in adjacent uplands by approximately 23%.North of treeline sites consistently contributed much greater error to basin-wide SWE estimates than sites to the south.The consistent regional differences were used to inform sampling strategies for each region.
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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 | 0.000 | 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".