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Record W2018564725 · doi:10.1109/aero.2008.4526260

The CloudSat Mission and the A-Train: A Revolutionary Approach to Observing Earth's Atmosphere

2008· article· en· W2018564725 on OpenAlex
D. Vane, Graeme L. Stephens

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings - IEEE Aerospace Conference · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsnot available
FundersCanadian Space AgencyU.S. Air ForceCalifornia Institute of TechnologyNational Aeronautics and Space Administration
KeywordsSatelliteRemote sensingLidarConstellationRadarEnvironmental scienceMeteorologyAtmosphere (unit)Profiling (computer programming)Cloud computingSatellite constellationComputer scienceAerospace engineeringGeologyGeographyEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

On April 28, 2006 a millimeter radar system, designed expressly for the vertical profiling of hydrometeors, was launched from Vandenburg Air Force Base. Both Cloudsat, carrying the cloud profiling radar (CPR), and the lidar satellite CALIPSO, were inserted into nearly identical orbits each approximately one minute behind the NASA Earth Observing System (EOS) Aqua satellite and in formation with the French PARASOL satellite and the EOS Aura satellite. This creates the A- Train satellite constellation. The early results of the CloudSat mission underscore the value of synergy of the A- Train observations for studying clouds and precipitation.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.022
GPT teacher head0.210
Teacher spread0.187 · 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