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Record W2089411265 · doi:10.1109/tim.2012.2234398

TIM Special Section on IEEE International Symposium on Robotic and Sensors Environments (ROSE 2011)

2012· article· en· W2089411265 on OpenAlexaffabout
Pierre Payeur, Emil M. Petriu

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2012
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRose (mathematics)RoboticsRelevance (law)AutomationEngineeringInstrumentation (computer programming)Process (computing)RobotComputer scienceArtificial intelligenceLibrary scienceEngineering managementOperations researchPolitical scienceMechanical engineering

Abstract

fetched live from OpenAlex

IEEE International Symposium on Robotic and Sensors Environments (ROSE 2011) was held on September 17-18, 2011, at Ecole Polytechnique de Montreal, in Montreal, QC, Canada. ROSE 2011 was the first opportunity for the conference series to be conducted as an IEEE Symposium, following a sustained and successful number of ROSE Workshop venues from 2003 to 2010, that took place in Europe, Canada, and the USA. Following the tradition established in 2003, the ROSE Symposium addresses all aspects of sensing systems and technologies for robotics and industrial automation, as well as their impact on autonomous robotic and intelligent systems development and applications. At ROSE 2011, 41 papers presenting contributions of high quality were presented, addressing the diversity of problems and applications that combine sensors, decision systems, and robots. Following the symposium, a number of substantially extended manuscripts, inspired by papers presented at ROSE 2011, and addressing topics of particular relevance for the IEEE Transactions on Instrumentation and Measurement, were submitted and went through a rigorous peer review process, identical to that of regular paper submissions. Following this process, three manuscripts were accepted and are included in this Special Section.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.247
Teacher spread0.204 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2012
Admission routes2
Has abstractyes

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