MétaCan
Menu
Back to cohort
Record W1993471283 · doi:10.1080/1463922x.2014.984012

Maximum forces and joint stability implications during in-line arm pushes

2014· article· en· W1993471283 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.

Bibliographic record

VenueTheoretical Issues in Ergonomics Science · 2014
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsMoment (physics)ElbowIsometric exerciseLine (geometry)Joint stabilityJoint (building)EngineeringBalance (ability)ElectromyographySimulationPhysical medicine and rehabilitationStructural engineeringMathematicsControl theory (sociology)Computer sciencePhysical therapyPhysicsMedicineAnatomyGeometryArtificial intelligence

Abstract

fetched live from OpenAlex

Current ergonomic software packages have no way of determining manual arm strength limits when the force vector passes directly through the elbow and shoulder, such that the moment demands on those joints are very low. The first step in overcoming this dilemma is to understand what the arm force limitations are when there is no moment to balance. Sixteen participants generated isometric in-line arm forces at varying intensities. Surface electromyography was used to monitor individual muscle activity, which was used in combination with moment arm estimates, to make inference about elbow stability. There was a significant difference in the average maximum forces produced by males compared to females, such that the 25th percentile maximum in-line arm push force is suggested to be 636 N for males and 359 N for females. Our force limits can be easily implemented into any ergonomic assessment tool as boundaries for maximum in-line arm forces.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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