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Record W2252465257 · doi:10.1002/2015gl067618

First observations of triple‐frequency radar Doppler spectra in snowfall: Interpretation and applications

2016· article· en· W2252465257 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

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsMcGill University
FundersDeutscher Akademischer AustauschdienstNatural Environment Research CouncilSight Research UK
KeywordsSnowSpectral lineRadarDoppler effectDoppler radarEnvironmental scienceMeteorologyPhysicsRemote sensingPrecipitationComputational physicsGeologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Abstract The potential of multifrequency Doppler spectra to constrain precipitation microphysics has so far only been exploited for dual‐frequency spectra in rain. In this study, we extend the dual‐frequency concept to triple‐frequency Doppler radar spectra obtained during a snowfall event which included rimed and unrimed snow aggregates. A large selection of spectra obtained from low‐turbulence regions within the cloud reveals distinctly different signatures of the derived dual spectral ratios. Due to the third frequency, a characteristic curve can be derived which is almost independent of the underlying particle size distribution and velocity‐size relation. This approach provides new opportunities for validating existing and future snow scattering models and reveals how the information content of triple‐frequency radar data sets can be further exploited for snowfall studies.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.054
GPT teacher head0.285
Teacher spread0.232 · 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