Bibliographic record
Abstract
In early January 2021, Spain was affected by two extreme events – an unusually long cold spell and a heavy snowfall event associated with extratropical cyclone Filomena. For example, up to 50 cm of snow fell in Madrid and the surrounding areas in 4 days. Already during 9 days prior to the snowfall event, anomalously cold temperatures at 850 hPa and night frosts prevailed over large parts of Spain. During this period, anomalously cold and dry air was transported towards Spain from central Europe and even from the Barents Sea. The storm Filomena, which was responsible for major parts of the snowfall event, developed from a precursor low-pressure system over the central North Atlantic. Filomena intensified due to interaction with an upper-level potential vorticity (PV) trough, which was the result of anticyclonic wave breaking over Europe. In turn, this wave breaking was related to an intense surface anticyclone and upper-level ridge, whose formation was strongly influenced by a warm conveyor belt outflow of a cyclone off the coast of Newfoundland. The most intense snowfall occurred on 09 January and was associated with a sharp air mass boundary with an equivalent potential temperature difference at 850 hPa across Spain exceeding 20 K. Overall, the combination of pre-existing cold surface temperatures, the optimal position of the air mass boundary, and the dynamical forcing for ascent induced by Filomena and its associated upper-level trough were all essential – and in parts physically independent – ingredients for this extreme snowfall event to occur.
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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 | 0.017 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.224 | 0.128 |
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".