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Record W2142688908 · doi:10.1175/jtech-d-15-0074.1

An Improved Liquid Water Absorption Model at Microwave Frequencies for Supercooled Liquid Water Clouds

2015· article· en· W2142688908 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 Atmospheric and Oceanic Technology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicrowaveAbsorption (acoustics)Liquid water contentRadiometerLiquid waterSupercoolingAttenuation coefficientPermittivityMaterials scienceComputational physicsEnvironmental scienceAbsorption of waterMicrowave radiometerField (mathematics)ThermodynamicsOpticsPhysicsCloud computingMathematicsDielectric

Abstract

fetched live from OpenAlex

Abstract An improved liquid water absorption model is developed for frequencies between 0.5 and 500 GHz. The empirical coefficients for this model were retrieved from a dataset that consists of both laboratory observations of the permittivity of liquid water (primarily at temperatures above 0°C) and field observations collected by microwave radiometers in three separate locations with observations at temperatures as low as −32°C. An optimal estimation framework is used to retrieve the model’s coefficients. This framework shows that there is high information content in the observations for seven of the nine model coefficients, but that the uncertainties in all of the coefficients result in less than 15% uncertainty in the liquid water absorption coefficient for all temperatures between −32° and 0°C and frequencies between 23 and 225 GHz. Furthermore, this model is more consistent with both the laboratory and field observations over all frequencies and temperatures than other popular absorption models.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

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 head0.010
GPT teacher head0.217
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