NATO Research Task Group: 3D Scanning for Clothing Fit and Logistics
Bibliographic record
Abstract
Military organizations require accurate information on the relationship between body size and shape to ensure proper fit of clothing and personal equipment.From an operational perspective, proper fit is essential for soldier clothing and equipment ensembles to function as designed, allowing optimal mobility, comfort and protection from environmental and ballistic threats.Meeting these requirements is challenging, as operational uniforms and ceremonial wear must be provided to all military members.Custom tailoring is provided for individuals of extreme body size, but this practise is expensive and undesirable.Additionally, secular changes in body size, and increasing ethnic diversity and presence of women in operational trades, including combat arms, has presented a challenge to military departments responsible for clothing specification, procurement and issuing.In response, the North Atlantic Treaty Organization Science and Technology Organization (NATO STO) has recognized the potential of 3D body scanning as a tool to rapidly acquire 3D anthropometry and body shape data to support clothing design and issuing.This has led to the establishment of NATO Research Task Group (RTG) HFM-266: 3D scanning for clothing fit and logistics.Currently, this Task Group is comprised of 9 member nations and one ally.The outcomes of this Task Group will serve to provide a better understating of the application of 3D body scanning technologies for military clothing and equipment application and inform the development of clothing sizing standards across NATO countries.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.020 |
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".