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Record W2604692953

Consortium for Robotics and Unmanned Systems Education and Research (CRUSER): FY11 Annual Report

2011· article· en· W2604692953 on OpenAlexfundno aff
Lyla Englehorn

Bibliographic record

VenueCalhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School) · 2011
Typearticle
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsnot available
FundersNaval Air Warfare Center, Aircraft DivisionAir Force Institute of TechnologyLawrence Livermore National LaboratoryOffice of Naval ResearchJapan Aerospace Exploration AgencyNaval Air Warfare Center, Weapons DivisionU.S. Naval Research LaboratoryUnited States Marine CorpsDirektoratet for UtviklingssamarbeidNaval Air Systems CommandJet Propulsion LaboratoryDefense Threat Reduction AgencyMinistry of Economy, Trade and IndustryTransport CanadaLangley Research CenterNational Oceanic and Atmospheric AdministrationDefense Advanced Research Projects AgencyUniversity of MemphisUniversity at BuffaloCalifornia State University, Monterey BayFederal Emergency Management AgencyH. Lee Moffitt Cancer Center and Research InstituteUniversity of DaytonUniversity of Notre DameUniversity of South CarolinaNational Science FoundationOklahoma State UniversityUniversity of MinnesotaWichita State UniversityWake Forest UniversityUniversity of Nevada, Las VegasU.S. Department of StateUtah State UniversityNational Aeronautics and Space AdministrationUniversity of South FloridaNaval Sea Systems CommandU.S. Department of EnergyUniversities Space Research AssociationU.S. Department of DefenseLeidosUniversity of PittsburghU.S. Department of Homeland SecuritySandia National LaboratoriesUnited States Special Operations CommandU.S. Department of JusticeUniversity of Oklahoma
KeywordsRoboticsArtificial intelligenceComputer scienceAeronauticsEngineeringRobot
DOInot available

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.076
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0650.039

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 head0.051
GPT teacher head0.289
Teacher spread0.237 · 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
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
Published2011
Admission routes1
Has abstractyes

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