MétaCan
Menu
Back to cohort
Record W2529641732 · doi:10.1016/j.adhoc.2016.09.020

Localization in wireless sensor networks: A Dempster-Shafer evidence theoretical approach

2016· article· en· W2529641732 on OpenAlexfundno aff
Colin Elkin, Rajika Kumarasiri, Danda B. Rawat, Vijay Devabhaktuni

Bibliographic record

VenueAd Hoc Networks · 2016
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of CanadaGeorgia State UniversityNational Aeronautics and Space AdministrationCummings FoundationNational Science Foundation
KeywordsDempster–Shafer theoryWireless sensor networkSensor fusionComputer scienceNode (physics)Set (abstract data type)Signal strengthSampling (signal processing)AlgorithmData miningArtificial intelligenceEngineeringTelecommunicationsComputer network

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.012
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.062
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0110.011
Science and technology studies0.0020.008
Scholarly communication0.0040.012
Open science0.0040.004
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.010
GPT teacher head0.213
Teacher spread0.202 · 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 designTheoretical or conceptual
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

Citations30
Published2016
Admission routes1
Has abstractno

Explore more

Same venueAd Hoc NetworksSame topicIndoor and Outdoor Localization TechnologiesFrench-language works237,207