Development of the AFRL CAESAR Web User Interface
Bibliographic record
Abstract
<div class="htmlview paragraph">Civilian American and European Surface Anthropometry Resource (CAESAR) (<span class="xref">Robinette, et al., 2002</span>) is an anthropometric database containing the latest civilian population survey of three countries representing the NATO countries: the United States of America, The Netherlands, and Italy. There are many potential applications for the CAESAR database in the anthropometry, ergonomics, and biometrics fields because it provides individual and standardized one-mode data instead of summarized population information such as percentiles. However, people are not using CAESAR more frequently because it is too difficult to access at present. To facilitate the sharing of this valuable resource, the CARD Lab (Computerized Anthropometric Research and Design Laboratory) in the Air Force Research Laboratory has been developing a web application, ARIS (Anthropometry Research Information Systems), to offer CAESAR data search and analysis as well as raw data visualization and extraction. ARIS consists of two components. The front-end user interface was designed for two groups of potential users. An atlas-type graphics interface targets casual users, and a menu-driven detailed search interface satisfies the needs of advanced users. The back-end database was designed to handle not only the CAESAR database but also other anthropometric databases collected by the CARD Lab over the years. The objective is to provide a new capability to make the right anthropometric information available to anyone, anywhere.</div>
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.074 |
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".