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Record W4248876598 · doi:10.1109/i2mtc.2018.8409516

Technical papers table of contents

2018· article· en· W4248876598 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Sensor Networks for Data Analysis
Canadian institutionsnot available
FundersWestern Sydney UniversityUniversidade Federal da ParaíbaUniversità degli Studi della Campania Luigi VanvitelliIstituto Nazionale di Ricerca MetrologicaNational Taipei University of TechnologyOulun YliopistoUniversità di BolognaUniversity of BalamandTallinna TehnikaülikoolSouth China University of TechnologyUniversità degli Studi di PalermoUniversidade Federal do Rio Grande do SulZhejiang UniversityTianjin UniversityÚstav termomechaniky, Akademie Věd České RepublikyUniversità degli Studi di TrentoUniversity of South CarolinaIndian Institute of Technology MadrasUniversità di PisaHarbin Institute of TechnologyEidgenössische Technische Hochschule ZürichUniversity of Western SydneyUniversitatea Politehnica TimisoaraChulalongkorn UniversityTU Graz, Internationale Beziehungen und MobilitätsprogrammeDurham UniversityUniversità degli Studi di MessinaUniversità Politecnica delle MarcheSveučilište u ZagrebuNewcastle UniversityBeijing Jiaotong UniversityCERNMcMaster UniversityUniversity of OttawaUniversity of OklahomaUniversità di CataniaMissouri University of Science and TechnologyTechnische Universität ChemnitzUniversität RostockTsinghua UniversityUniversità degli Studi di BresciaPolitecnico di TorinoState Key Laboratory of Industrial Control TechnologyUniversità degli Studi di Napoli Federico IIUniversity of Oklahoma Health Sciences CenterSeconda Università degli Studi di NapoliUniversità degli Studi di Cagliari
KeywordsComputer scienceTable (database)Table of contentsInformation retrievalWorld Wide WebDatabase

Abstract

fetched live from OpenAlex

The following topics are dealt with: condition monitoring; calibration; vibrations; feature extraction; learning (artificial intelligence); fault diagnosis; fibre optic sensors; patient monitoring; temperature measurement; medical signal processing.

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)0.004
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 score0.358
Threshold uncertainty score0.511

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.6420.633

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 head0.239
Teacher spread0.223 · 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
Published2018
Admission routes1
Has abstractyes

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