Measurement of Handgrip Strength of North Indian Male Farmers and Its Implications in Design of Farm Equipment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".