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Record W3152089986 · doi:10.1016/j.apergo.2021.103415

A comparison between measured female linear arm strengths and estimates from the 3D Static Strength Prediction Program (3DSSPP)

2021· article· en· W3152089986 on OpenAlexaff
Andrew D. Hall, Nicholas J. La Delfa, Chris M. Loma, Jim R. Potvin

Bibliographic record

VenueApplied Ergonomics · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsFanshawe CollegeOntario Tech UniversityMcMaster University
Fundersnot available
KeywordsRange (aeronautics)Joint (building)Linear relationshipStatisticsMathematicsPhysical medicine and rehabilitationStructural engineeringMedicineEngineering

Abstract

fetched live from OpenAlex

This study performed a direct comparison between empirically measured female linear arm strengths and those estimated with the 3D Static Strength Prediction Program (3DSSPP). Linear arm strengths were collected from 15 female participants, at four hand locations and six primary directions (n = 360), and then estimated with 3DSSPP incorporating each participant's own segment lengths, body masses and joint strengths, and the measured arm postures from each trial to optimize the accuracy of 3DSSPP. In spite of this, the errors in 3DSSPP's estimated arm strength values were very high (RMS error = 56.0 N and 40.4%) and poorly correlated (r2 = 29.2%) with measured strengths. These results seriously question the accuracy of 3DSSPP to estimate female linear arm strengths and percent capable values, for the range of conditions tested, likely due to the overly simplified assumptions made to estimate triaxial shoulder strength.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.597

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 head0.310
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2021
Admission routes1
Has abstractyes

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