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Record W1503756833 · doi:10.1109/iembs.2006.260206

Design and Validation of a Robotic Needle Positioning System for Small Animal Imaging Applications

2006· article· en· W1503756833 on OpenAlex
Adam C. Waspe, H. Jason Cakiroglu, James C. Lacefield, Aaron Fenster

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsComputer visionOrientation (vector space)Artificial intelligenceComputer sciencePositioning systemRobotPosition (finance)Point (geometry)Rotation around a fixed axisAcousticsBiomedical engineeringEngineeringPhysicsMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

A needle-positioning robot has been developed for image-guided interventions in small animals. The device is designed to position a needle with an error < or =100 microm. The robot has two rotational axes (pitch and roll) to control needle orientation, and one linear axis to perform needle insertion. The three axes intersect at a single point, creating a remote center of motion. Needle positioning error was quantified at ten target locations for each rotational plane. The measured needle positioning accuracy in free space was 54 +/-12 microm and 91 +/- 21 microm for the pitch and roll axes, respectively. The device's accuracy compares favorably with the sizes of typical interventional targets in mouse models.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.275

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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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

Quick stats

Citations6
Published2006
Admission routes1
Has abstractyes

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