MétaCan
Menu
Back to cohort
Record W2071565057 · doi:10.1007/s11214-014-0130-y

The e-POP Radio Receiver Instrument on CASSIOPE

2015· article· en· W2071565057 on OpenAlexafffund
H. G. James, E. P. King, Andrew P. White, R. H. Hum, W. H. H. L. Lunscher, C. L. Siefring

Bibliographic record

VenueSpace Science Reviews · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsCOM DEV InternationalUniversity of Calgary
FundersCanadian Space AgencyUniversity of Calgary
KeywordsRadio receiver designPreamplifierBandwidth (computing)AcousticsIntermediate frequencyPhysicsIonosphereRadio frequencySIGNAL (programming language)Radio waveRemote sensingElectrical engineeringTransmitterComputer scienceChannel (broadcasting)TelecommunicationsEngineeringAmplifierGeology

Abstract

fetched live from OpenAlex

The Radio Receiver Instrument (RRI) is a four-channel digital receiver fed by four 3-metre monopoles, arranged in a crossed configuration, each connected to a high input impedance preamplifier. The RRI bandwidth extends from 10 Hz to 18 MHz. The receiver measures the electric fields of either spontaneous radio emissions or waves created by ground transmitters, such as ionosondes, high-frequency radars and ionospheric heaters. In order to measure accurately the intensity, frequency, direction of propagation and signal delay of such fields over the broad frequency range, modern digital receiver technology is employed. The amplified signals from the monopoles are digitized at a rate of 40 megasamples per second, and from there on, the signal is down-converted, filtered, time-stamped and communicated in digital form. The characterization results of the RRI flight model are reported. Formats for data commands for configuring the digital receiver and for data output are described.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.011

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 head0.031
GPT teacher head0.282
Teacher spread0.251 · 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
GenreMethods

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

Citations47
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueSpace Science ReviewsSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207