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Record W4212892434 · doi:10.1002/essoar.10510542.1

Investigation of liquid cloud formation mechanisms during the Arctic ozone-depletion events of the 2010/2011, 2015/2016 and 2019/2020 NH winter seasons

2022· preprint· en· W4212892434 on OpenAlexafffund
Liviu Ivănescu, Keyvan Ranjbar, Norman T. O’Neill

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité de Sherbrooke
FundersCanadian Space Agency
KeywordsArcticCloud computingOzone depletionThe arcticOzoneMeteorologyAtmospheric sciencesComputer sciencePhysicsOceanographyGeologyOperating system

Abstract

fetched live from OpenAlex

The unusually cold springtime Arctic stratospheres of 2011, 2016 and 2020 generated substantial Polar Stratospheric Clouds (PSCs) activity and a significant ozone hole. These events were accompanied by an unusual presence of precipitating liquid clouds in the high Arctic. Satellite lidar measurements helped to identify a possible mechanistic link between tropospheric cloud formation and the PSCs. The synoptic meteorological context provided by the ERA 5 reanalysis was instrumental in the identification of potential liquid-precipitation formation scenarios related to atmospheric rivers.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.201
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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