Estimation of Snow Reserves in Watercourses in the Arctic Region
Bibliographic record
Abstract
For a reliable estimate of snow reserves in theArctic Archipelagoproper allowance must be made for snow accumulation in the areas of relief lowering such as riverbeds, ravines and canyons. As applied to calculating the water yield from the catchment area, unaccounted reserves in the channels may be even larger than recorded. For thickness of the snow cover on similar objects measuring was used Picor-Led ground-penetrating radar. Test measurements of the thickness of the snow cover performed both by radar and manually showed good repeatability of measurements. Data on snow reserves in the catchments of the Mushketov and Amba rivers were obtained using the radar method for northern part of Bolshevik Island in spring 2017. Measurements results has revealed significant differences in the amount of snow reserves between the plateau sections and the river valleys. The thickness of seasonal snow cover in the riverbeds and canyons varied widely and reached10.5 metersdepth. The average values of snow thickness cover on the plateau and in the riverbeds of the Mushketov and Amba rivers were 0.37, 1.80 and1.86 mrespectively during the period of maximum snow accumulation. Our estimates showed that snow deposits in riverbeds have specific snow reserves 6.5–7.5 times higher than specific snow reserves on the plateau. In addition,
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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 | 0.002 | 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) | 0.000 | 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".