Proposal of initial parameters for an anthropometric database of the Honduran working population
Bibliographic record
Abstract
In Honduras, Central America, there is a shortage of anthropometric studies conducted in the workplace, making it necessary to establish initial parameters.This research employed a quantitative approach that encompassed three cities: Tegucigalpa, San Pedro Sula, and El Progreso, taking into account 29 variables.Various techniques and tools, including anthropometric tapes and statistical methods, were utilized.The sample consisted of 60 volunteers from the three cities, selected through non-probabilistic convenience sampling.Measurements were collected in designated areas, involving a pilot phase and validation process.Averages calculated for each city highlighted the physical diversity present in the population.The final data provided maximum, minimum, and percentiles (5, 50, and 95) for the ergonomic design of workplaces.These outcomes stress the immediate necessity of anthropometric data in Honduras and endorse further research to enhance adaptability and workplace safety.The precision of pilot testing is of paramount importance.The El Progreso group exhibited distinct differences in 15 measurements.Comparing percentiles among the cities unveiled variations, particularly in stature.Certain measurements were identified as pivotal for ergonomic design.The substantial difference of up to 20 cm from U.S. tables emphasizes the requirement for specific tables in ergonomic studies.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".