Distribution of snow cover over Northern Eurasia
Bibliographic record
Abstract
Based on observation data the spatial variability and long-term trends of snow depth, snow water equivalent and number of days with snow coverage ≥50% for Northern Eurasia are estimated. The significance of continental snow cover variability over Northern Eurasia is illustrated by comparison with snow cover variability of the northern part of North America (Canada). The fundamental scientific problem of our investigations is revealing spatial and temporal changes of snow cover under the present climate conditions. The snow cover depends on a climate on the one hand and appreciably defines a hydrological regime on the other hand and, thus, the snow cover is a good indicator of changes in the condition of an environment. In this case the condition of the snow cover of the Northern hemisphere on an example of Northern Eurasia within the boundaries of the NIS and the northern part of North America within the boundaries of Canada is investigated. The novelty of the work, in particular, consists in the attraction to the analysis of a lot of long-term data on the snow cover of two continents. As a result the general regularity of spatial heterogeneity and the long-term variability of snow stocks were revealed.
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 | 0.000 | 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".