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Record W2159837973 · doi:10.1109/tmech.2009.2023648

Design and Implementation of a Micromanipulation System Using a Magnetically Levitated MEMS Robot

2009· article· en· W2159837973 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsMicroelectromechanical systemsLevitationRobotMagnetic levitationMagnetMagnetic fieldMechanical engineeringMaterials scienceGrippersNanotechnologyComputer scienceEngineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Magnetic levitation of microrobots is presented as a new technology for micromanipulation tasks. The microrobots were fabricated based on microelectromechanical systems technology and weigh less than 1 g. The robots can be positioned in 3-D using magnetic field. It is shown that microrobots can be produced using commercially available magnets or electrodeposited magnetic films. A photothermal microgripper is integrated to the microrobots to perform micromanipulation operations. The microgrippers can be actuated remotely by laser focusing that makes the microrobot free of any wiring. This leads to increased motion range with more functionality in addition to dust-free motion and ability to work in closed environments. The 3-D motion capability of the microrobots is verified experimentally and it was demonstrated that the microgrippers can be operated in a vertical range of 4 mm and a horizontal range of 4 mm times 5 mm. Micromanipulation experiments such as pick-and-place, pushing, and pulling were demonstrated using objects with 100 mum and 1 mm diameter.

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.

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: none
Teacher disagreement score0.829
Threshold uncertainty score0.736

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 head0.021
GPT teacher head0.272
Teacher spread0.252 · 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