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Record W2321690058 · doi:10.1109/tim.2016.2539458

Guest Editorial Special Issue on the 2015 IEEE International Instrumentation and Measurement Technology Conference Pisa, Italy, May 11–14, 2015

2016· editorial· en· W2321690058 on OpenAlexaff
Shervin Shirmohammadi, Pasquale Daponte, Wendy Van Moer

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2016
Typeeditorial
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsInstrumentation (computer programming)NotationGalileo (satellite navigation)Theme (computing)Face (sociological concept)Measure (data warehouse)EngineeringUnits of measurementComputer scienceLibrary scienceSystems engineeringSoftware engineeringWorld Wide WebProgramming languagePhysicsMathematicsRemote sensingGeographySociologyData miningAstronomy

Abstract

fetched live from OpenAlex

The 32nd annual IEEE International Instrumentation and Measurement Technology Conference ($\text{I}^{2}$MTC) was held in historic and beautiful Pisa, Italy. The theme of the conference was “The Measurable of Tomorrow: Providing a Better Perspective on Complex Systems.” The first part of the title embraced the challenge of Galileo Galilei, “Measure what is measurable, and make measurable what is not so,” while the second part called for the instrumentation and measurement (I&M) community to face the increasing complexity of systems resulting from the continuous development of new technologies.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.047
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0020.002
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0470.036

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 head0.039
GPT teacher head0.287
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2016
Admission routes1
Has abstractyes

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