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Record W1968570657 · doi:10.1080/00140130110109702

Characterizing human hand prehensile strength by force and moment wrench

2001· article· en· W1968570657 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

VenueErgonomics · 2001
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Waterloo
FundersWorkplace Safety and Insurance Board
KeywordsPrehensile tailWrenchDynamometerMoment (physics)Zero moment pointEngineeringInternal forcesTorqueComputer scienceSimulationMechanical engineeringStructural engineeringArtificial intelligencePhysicsRobotGeologyClassical mechanics

Abstract

fetched live from OpenAlex

Characterizing human hand capabilities or demand created by various occupational tasks or activities of daily living has been mainly accomplished by measuring the maximum force exerted on a force dynamometer in a number of standard grips, for example power, key pinch and tip pinch grips. A framework is proposed instead to characterize human hand prehensile strength in generic form by describing external force and moment wrench capability, where a wrench is a vector describing the forces and moments applied at a point. It is further suggested that if tools and activities are characterized by the internal forces and external forces and moments required, a better understanding of the human prehension in occupational settings and during activities of daily living can be obtained. An example of using a pistol grip drill is used to show the utility of the approach.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.436

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 head0.014
GPT teacher head0.216
Teacher spread0.203 · 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