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2A2-D04 Musculoskeletal Motion Analysis of Index Fingers by Using Cadaver Specimens(Musculoskeletal Modeling)

2012· article· en· W2706831838 on OpenAlexaff
Mitsunori Tada, Shinjiro Sueda, Yusaku Kamata, Toshiyasu Nakamura, Dinesh K. Pai, Takeo Nagura, Yoshiaki Toyama

Bibliographic record

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2012
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsExcursionCadaveric spasmRange of motionCadaverMotion analysisMedicinePhysical medicine and rehabilitationAnatomyOrthodonticsPhysical therapyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We present a new apparatus, excursion controller, to measure and analyze musculoskeletal motion of cadaveric fingers. The result of the preliminary experiment clearly shows that the range of excursion of the interosseous for abduction-adduction and that of the extensors for hyperextension were estimated to be 5 to 8mm, while that of the flexors for full-flexion were estimated to be 35mm from the reference posture.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.025
GPT teacher head0.289
Teacher spread0.264 · 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 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 routes1
Has abstractyes

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