Anthropometric Table Consolidation for the Honduran Population Database
Bibliographic record
Abstract
In Honduras, the availability of anthropometric tables is limited, leading Honduran companies to resort to international anthropometric tables that do not represent the body dimensions and needs of Honduran workers.We will carry out a consolidation of previous anthropometric studies developed in the following departments of Honduras: Francisco Morazán, Cortes, Yoro, Choluteca, El Paraíso, Olancho, Comayagua, Intibucá and La Paz.The research used a quantitative approach and analytical scope, since a relationship was sought between anthropometric measurements and their impact in the workplace in Honduras.To determine whether the segmentation of the Consolidation should be by region or a single representative table of the Honduran population could be made, a statistical pilot was applied taking 3 of the 9 departments measured, concluding that 66% of the Anthropometric measurements for men and 72% for women respectively in the departments of Francisco Morazán, Cortés and Yoro statistically present different means in their data.Therefore, a consolidation by region was carried out.To verify the validity of this consolidated database, a comparison was made with US tables to verify whether the population data differed as proposed at the beginning of the research.This comparison also confirmed the differences raised.This research was successfully validated through statistical piloting in Minitab and expert triangulation with three engineering advisors.The research concluded that this database was not completely representative of the Honduran population, leading to the continuation of measurements in the remaining nine departments to resume this consolidation using this research as a proposal and guide.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".