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Record W2022839575 · doi:10.1175/2007jas2479.1

Vertical Motions in Arctic Mixed-Phase Stratiform Clouds

2008· article· en· W2022839575 on OpenAlexaff
Matthew D. Shupe, Pavlos Kollias, Ola Persson, Greg M. McFarquhar

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsMcGill University
FundersOffice of ScienceU.S. Department of Energy
KeywordsLiquid water contentContext (archaeology)ArcticEnvironmental scienceIce crystalsAtmospheric sciencesIce nucleusCloud computingCloud physicsIce cloudCloud topLiquid water pathMeteorologyPrecipitationGeologyPhysicsNucleationOceanography

Abstract

fetched live from OpenAlex

Abstract The characteristics of Arctic mixed-phase stratiform clouds and their relation to vertical air motions are examined using ground-based observations during the Mixed-Phase Arctic Cloud Experiment (MPACE) in Barrow, Alaska, during fall 2004. The cloud macrophysical, microphysical, and dynamical properties are derived from a suite of active and passive remote sensors. Low-level, single-layer, mixed-phase stratiform clouds are typically topped by a 400–700-m-deep liquid water layer from which ice crystals precipitate. These clouds are strongly dominated (85% by mass) by liquid water. On average, an in-cloud updraft of 0.4 m s−1 sustains the clouds, although cloud-scale circulations lead to a variability of up to ±2 m s−1 from the average. Dominant scales-of-variability in both vertical air motions and cloud microphysical properties retrieved by this analysis occur at 0.5–10-km wavelengths. In updrafts, both cloud liquid and ice mass grow, although the net liquid mass growth is usually largest. Between updrafts, nearly all ice falls out and/or sublimates while the cloud liquid diminishes but does not completely evaporate. The persistence of liquid water throughout these cloud cycles suggests that ice-forming nuclei, and thus ice crystal, concentrations must be limited and that water vapor is plentiful. These details are brought together within the context of a conceptual model relating cloud-scale dynamics and microphysics.

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 score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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

Citations211
Published2008
Admission routes1
Has abstractyes

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