Bibliographic record
Abstract
Seasonal snow covers a large fraction of the Northern Hemisphere (NH) land area (and some Southern Hemisphere mountain regions) for periods of up to 10 months of the year and is an integral part of the landscape, climate, hydrology, ecology, and economy. A snow cover (duration, depth) and its physical properties (albedo, density, crystal structure, etc.) represent an integrated response of physical processes to a number of external climate/environmental drivers. Greenhouse gas‐induced warming and moistening of mid‐ to high latitudes, increased incoming longwave radiation, and Arctic green‐up are all drivers of a changing snow cover, and there is growing evidence from surface and satellite observations of significant changes in seasonal snow cover: significant declines in spring snow cover duration are observed across the NH, and significant decreases in winter snowpack are observed over mid‐latitudes, particularly coastal mountains. Trends in snow cover simulated by climate models are for the most part consistent with observations but underestimate the observed rates of decrease in Arctic spring snow cover. Climate models have improved but important sources of uncertainty remain related to snow albedo feedbacks, high‐latitude precipitation, and inadequate treatment of landscape‐dependent snow processes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".