MétaCan
Menu
Back to cohort

MGF Based Analysis of Area under the ROC Curve in Energy Detection

2011· article· en· W2057929897 on OpenAlexaff
Saman Atapattu, Chintha Tellambura, Hai Jiang

Bibliographic record

VenueIEEE Communications Letters · 2011
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFadingReceiver operating characteristicNakagami distributionMoment-generating functionMaximal-ratio combiningEnergy (signal processing)DetectorSignal-to-noise ratio (imaging)Detection theoryComputer scienceArea under curveFunction (biology)Noise (video)AlgorithmMoment (physics)Diversity combiningStatisticsTelecommunicationsMathematicsTopology (electrical circuits)Probability density functionChannel (broadcasting)PhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The area under the receiver operating characteristic (ROC) curve (AUC), an important performance measure of the energy detector, is derived for Nakagami-m and η-μ fading channels. The analysis is based on the moment generating function (MGF) of the received signal-to-noise ratio (SNR). The derived closed-form expressions do not include special functions, thus reducing computational issues. The analytical framework can also be applied in cases with other fading channels, with diversity reception, or with cooperative spectrum sensing.

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)0.018
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.110
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.003
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.049
GPT teacher head0.247
Teacher spread0.198 · 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 designSimulation or modeling
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

Citations39
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Communications LettersSame topicCognitive Radio Networks and Spectrum SensingFrench-language works237,207