Anthropometric Visual Data Mining: A Content-Based Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Whereas much of the previous work on anthropometric data mining has focused on mining measurements and statistical data, in this paper content-based anthropometric data mining of threedimensional scanned human bodies is discussed. A short review of digitalisation of human bodies is presented. Then it is shown how it is possible to describe the three-dimensional shape of the bodies by representing them with compact support feature vectors. A recurrent data mining system based on these vectors is then presented. This system allows to clustorize the population by using the 'query by example' paradigm and the knowledge of the expert in a recurrent approach. A virtual environment is then used in order to perform visual data mining on the clusters and to characterize the population by defining archetypes. The subject is covered both from the theoretical and implementation point of view. We discuss calculation of the feature vectors, recurrent query by example clustorization, database, virtual environments and definition of the archetypes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it