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Record W3108785673 · doi:10.18280/i2m.190509

Measurement of Handgrip Strength of North Indian Male Farmers and Its Implications in Design of Farm Equipment

2020· article· en· W3108785673 on OpenAlexvenueno aff
Sandeep Singh Kharb, R. M. Belokar, Suman Kant, Milap Sharma

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2020
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPercentileIsometric exerciseAgrarian societyAgricultureGrip strengthEnvironmental healthEngineeringMedicineOperations managementPhysical therapyMathematicsStatisticsGeography

Abstract

fetched live from OpenAlex

As a systematic ergonomics improvement process has always been prevailing in mankind to maximize the human performance within its body’s capabilities and limitations, by providing it a safe workplace and equipment. In addition to this custom, a scrutinized effort is made to associate worker's strength with the force/torque required to operate the tool/equipment in agrarian society of Haryana state (i.e. northern part of India). Therefore, an isometric hand grip strength for both hands (dominant and other one) base data of 200 male agricultural workers (age 18-60 years) from five districts has been measured with baseline handgrip dynamometer. However 20-50 years age group are found actively involved in arduous agricultural activities. On summarizing the Statistical information for the age group 20-50 years (173 subjects) such as mean, standard deviation (SD), skewness, kurtosis, 5th and 95th percentiles it has been observed that Dominant handgrip strength (46.14±7.13 kg) is significantly different (p<0.05) from the opposite handgrip strength (44.50±7.48 kg). Further study also reveals that the strength of handgrip declines significantly (p<0.05) with the increasing age.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.482

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 head0.067
GPT teacher head0.311
Teacher spread0.244 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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