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Record W2005438691 · doi:10.1109/toh.2014.2330619

Development of a Guaranteed Stable Network of Telerobots with Kinesthetic Consensus

2014· article· en· W2005438691 on OpenAlexafffund
Kamran Razi, Keyvan Hashtrudi-Zaad

Bibliographic record

VenueIEEE Transactions on Haptics · 2014
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKinesthetic learningComputer scienceEngineeringPsychology

Abstract

fetched live from OpenAlex

We present the research advances on the development of 50-200 mJ energy range diode-pumped Yb:CaF2- based multipass amplifiers operating at relatively high repetition rates. These laser amplifiers are based on diverse innovative geometries. All these innovations aim to design compact, stable and reliable amplifiers adapted to our application that consists in pumping ultrashort-pulse OPCPA (optical parametric chirped pulse amplifier) systems in the frame of the Apollon 10 PW laser project. The targeted repetition rate is in the range of 20-100 Hz with energies of few tens of mJ for the first stages up to 1 J for the final stage. An analysis of the specificities of Yb:CaF2 is done to explain the different options we chose to fulfil these specifications. The critical points and limitations of the multipass Yb:CaF2-based amplifiers are subsequently discussed. To overcome the encountered problems, different issues are investigated such as crystal optimisation, laser head geometry, thermo-optical dynamics or coherent combining techniques. Experimental results for different multipass configurations are demonstrated and discussed.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.014
GPT teacher head0.195
Teacher spread0.181 · 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 designSimulation or modeling
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

Citations3
Published2014
Admission routes2
Has abstractyes

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