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Record W2129406342 · doi:10.1109/pacrim.1997.619939

Delivery of weather image information to a mobile platform using satellite broadcast

2002· article· en· W2129406342 on OpenAlexaff
J.E. Jordan, D. Marcotte

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsWeather satelliteGeostationary orbitRemote sensingComputer scienceRetransmissionSatellitePixelCommunications satelliteSatellite systemEnvironmental scienceGlobal Positioning SystemReal-time computingMeteorologyTelecommunicationsGeologyGeographyEngineeringTransmission (telecommunications)Artificial intelligenceGNSS applicationsAerospace engineering

Abstract

fetched live from OpenAlex

This paper focuses on work in the delivery of weather information to a mobile platform using satellite communications. A weather satellite imaging system for a meteorological research aircraft is described, which receives 4 km/pixel resolution imagery, transmitted by polar-orbiting weather satellites at 137 MHz. The system uses a personal computer with receiver and decoder boards and imaging software as well as a novel low-profile "electrically small" (16 inches square) patch antenna for the aircraft. The system could also be used in the marine environment to receive weather information not readily available in remote areas by other means. Reception of higher resolution (1< km/pixel) imagery available from a variety of geostationary and polar orbiting satellites in-flight during field experiments is a current research objective. Investigation of the retransmission of images from a ground receiving station or the Internet using satellite communications facilities is currently in progress.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.229
Teacher spread0.197 · 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 designNot applicable
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
Published2002
Admission routes1
Has abstractyes

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