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Record W2797862228 · doi:10.1051/epjconf/201817605051

Retrievals of ice-water content from an airborne elastic lidar in tropical convective clouds

2018· article· en· W2797862228 on OpenAlexafffund
Konstantin Baibakov, Mengistu Wolde, Cuong Nguyen, Alexei Korolev, Ivan Heckman

Bibliographic record

VenueEPJ Web of Conferences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsEnvironment and Climate Change CanadaNational Research Council Canada
FundersEnvironment and Climate Change Canada
KeywordsLidarMesoscale meteorologyContext (archaeology)Environmental scienceRadiative transferInstrumentation (computer programming)Remote sensingMeteorologyIce waterAtmospheric sciencesConvectionWater contentLiquid water contentGeographyGeologyCloud computingPhysics

Abstract

fetched live from OpenAlex

Ice water content (IWC) is one of the critical parameters in determining the cloud radiative impact. In this work lidar-based IWC retrievals obtained in tropical mesoscale convective systems are evaluated in the context of an extensive in-situ and remote sensing instrumentation suite. Based on a test case of May 27, 2015 lidar-derived IWC values at 50 m above the aircraft were on average within 25% of the in-situ IWC measurements obtained using an isokinetic probe.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.025
GPT teacher head0.250
Teacher spread0.226 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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