Bibliographic record
Abstract
Atmospheric rivers (ARs) are long, narrow, and transient corridors of robust horizontal water vapor transport commonly associated with a low-level jet stream ahead of the cold front of an extratropical cyclone. These weather features are essential for Earth’s hydrological cycle, transporting water vapor poleward, delivering precipitation for local climates, and having societal repercussions, such as intense storms and flood risk. The polar regions have experienced increasing AR activity in recent years. ARs usually transport substantial amounts of moisture and heat poleward that can potentially affect glaciers and sea ice. Many studies have demonstrated that ARs cause surface melting of glaciers in Antarctica and Greenland. Predicting and understanding the characteristics of ARs under global warming is a challenging task because there is not a consensus among scientists on a quantitative definition of ARs and the tracking methods. Understanding how ARs affect the surface mass balance of glaciers is crucial to increase our knowledge of how a warming atmosphere associated with warm ocean water will impact glaciated areas. In this work, we review recent advances in AR, including the methods used to identify them, their impacts on glaciers, their relationship with large-scale ocean-atmosphere dynamics, and variabilities under future climate.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".