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Record W2020307155 · doi:10.1175/jas3563.1

Modeling of the Melting Layer. Part III: The Density Effect

2005· article· en· W2020307155 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsSnowSnowflakePrecipitationSupercoolingRadarAtmospheric sciencesLiquid water contentAtmosphere (unit)Rain and snow mixedEnvironmental scienceFlux (metallurgy)MeteorologyMaterials scienceGeologyPhysicsCloud computing

Abstract

fetched live from OpenAlex

Abstract A model of the melting snow and its radar reflectivity is presented here. The main addition to previous description of the melting layer is the explicit introduction of snow density as a variable. The model is validated with radar observations. Differences in brightband intensity for comparable precipitation rates are related here to the coexistence of supercooled cloud water (SCW) with snow above the melting level leading to riming and change in snow density. Cases where riming was suspected were selected according to the characteristics of the vertical profile of reflectivity flux above the melting layer and vertical Doppler velocities faster than expected from low-density snow. For stratiform precipitation with a melting layer, high snow-to-rain velocity ratio indicates high-density snow and consequently a small peak-to-rain reflectivity difference is expected. This relationship was computed from the model and confirmed with vertically pointing radar observations. In spite of the complexity of the physical processes present in the melting layer the model appears to capture the essential elements.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.237
Teacher spread0.207 · 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