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Record W4245049344 · doi:10.1109/glocom.2012.6503055

Table of contents

2012· article· en· W4245049344 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsnot available
FundersInstituto Federal de Alagoas, Ministério da EducaçãoInstitute of Computing Technology, Chinese Academy of SciencesUniversity of California, Los AngelesWaterford Institute of TechnologyInstituto de TelecomunicaçõesKeio UniversityUniversidade Federal do Rio de JaneiroNanjing Institute of TechnologyUniversidade de PernambucoUniversity of WaterlooNanjing UniversityUniversitat Politècnica de CatalunyaUniversity of Chinese Academy of SciencesDartmouth CollegeUniversidade Federal de PernambucoUniversité de StrasbourgNanjing University of Posts and TelecommunicationsSoutheast UniversitySouth Dakota School of Mines and TechnologyZhejiang UniversityUniversité de LiègeGeorgia Institute of TechnologyBeijing University of Posts and TelecommunicationsUniversidad de NavarraChinese Academy of SciencesUniversity of the AegeanTexas Tech UniversitySouth Dakota State UniversityPeople's Liberation Army University of Science and TechnologyUniversidad Pública de NavarraUniversity of California, IrvineUniversidade da Beira Interior
KeywordsTable (database)Computer scienceDatabase

Abstract

fetched live from OpenAlex

The following topics are dealt with: ad hoc networking; sensor networking; communication security; information system security; cognitive radio; cognitive networks; communications QoS; communication reliability; communication modelling; communication software; multimedia communications; communication theory; next generation networking; Internet; optical networks; green systems; signal processing; wireless communication; and wireless networking.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.210
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7900.713

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 head0.020
GPT teacher head0.225
Teacher spread0.205 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2012
Admission routes1
Has abstractyes

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