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Record W2062168089 · doi:10.1086/519563

Control and Communications System for Remote Operation of an Infrared Radiometer

2007· article· en· W2062168089 on OpenAlexafffund
Ian Schofield, David A. Naylor

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2007
Typearticle
Languageen
FieldEngineering
TopicAstronomical Observations and Instrumentation
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRemote sensingRadiometerInfraredComputer scienceWater vaporSoftwareInterferometryInterface (matter)Atmosphere (unit)OpacityData acquisitionEnvironmental sciencePhysicsOpticsMeteorologyGeologyOperating system

Abstract

fetched live from OpenAlex

An infrared radiometer (the Infrared Radiometer for Millimetre Astronomy, IRMA) has been developed to measure the amount of water vapor in the atmosphere through its emission at 20 μm. Water vapor is the major contributor to signal phase error in submillimeter interferometric arrays and the principal source of opacity for telescopes operating at infrared wavelengths. While earlier versions of IRMA required hands‐on operation, the desire to operate at remote and often hostile sites necessitated the development of sophisticated and robust software. IRMA is a distributed, real‐time control and data acquisition system spread across three different computers, and can be controlled remotely over the network by command scripts or a graphical user interface client program.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0620.029

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 head0.015
GPT teacher head0.225
Teacher spread0.210 · 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 designBench or experimental
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

Citations3
Published2007
Admission routes2
Has abstractyes

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