Measurement of Anthropometric Parameters for the Honduran Population Database Using Photogrammetry
Bibliographic record
Abstract
Honduras lacks its own anthropometric data, making workspaces dysergonomic and increasing the risk of musculoskeletal injury.Therefore, this research has created an anthropometric database for three departments in Honduras: Comayagua, Intibucá and La Paz.Taking a quantitative approach with a correlational scope integrating twenty-nine variables representing the body dimensions of people.The study population consisted of 60 people with a non-probabilistic convenience sample.For a better understanding of the distribution of anthropometric data, the 5th, 50th and 95th percentiles were calculated, providing a guide to design products adapted to Honduran characteristics.The results reveal that men are on average 7.1% taller than women, which is equivalent to a difference of 12.3 cm.However, when considering aspects beyond gender, significant variations are observed within cities due to ethnic diversity.The piloting allowed to reduce the time and margin of error in the measurements, allowing to optimize the time in a 60% faster and efficient way.This study was validated by triangulation, Repeatability and Reproducibility and Technical Measurement Error.The results were found to be within the allowed percentage of variation, which is less than 10% and within the maximum allowable error of 2%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".