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Record W1968275842 · doi:10.1109/dyspan.2010.5457842

Cognitive, Radio-Aware, Low-Cost (CORAL) Research Platform

2010· article· en· W1968275842 on OpenAlexaff
John Sydor, Amir Ghasemi, Siva Palaninathan, William Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsCognitive radioWirelessComputer scienceWhite spacesCognitive networkInterference (communication)Wireless networkRadio resource managementComputer networkTelecommunicationsChannel (broadcasting)CognitionPsychology

Abstract

fetched live from OpenAlex

Recently, the topic of cognitive radio has spurred a lot of interest within both academic and regulatory circles. Cognitive radios are envisioned to be capable of improving spectrum utilization as well as coexistence among wireless networks. However, a relatively low-cost research platform for implementation and demonstration of cognitive radio concepts in a typical wireless network scenario is still lacking. To address this issue, we have developed the CORAL platform which modifies the operation of low-cost wireless devices to collectively perform the essential tasks of a cognitive radio network such as radio environment awareness, white space selection, and co-channel interference avoidance. This demonstration will showcase the CORAL platform and its main cognitive radio features.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.830
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.111
GPT teacher head0.378
Teacher spread0.267 · 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 teacher head, not a consensus.

Study designOther design
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

Citations11
Published2010
Admission routes1
Has abstractyes

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