Test methods for protective footwear against AP mine blast
Bibliographic record
Abstract
The testing and development of protective footwear for anti-personnel landmine blast threats is of great importance to civilian and military deminers, and peacekeepers. This study will review the wide range of test methods that have been developed by NATO countries to test footwear against the effects of anti-personnel blast mines. Experimental testing requires the definition of a threat and a means of assessing the expected trauma to the human leg. The latter is accomplished with various physical models to represent the human leg. These models include simple metal columns, mechanical legs, frangible legs and biological specimens. Each model has advantages and disadvantages, and the choice must be guided by the scope and purpose of a given test series. In some cases, it is necessary to use a frangible model, but there are many cases where using a non-frangible model may be more appropriate. In addition to the physical test methods, computer modeling is providing a powerful tool to analyse and interpret test results. Advances have been made recently with respect to numerical code applications and some applications will be presented. This paper is one of four related multi-national papers, presented by the members of the NATO HFM-089/TG-024, to address all aspects of the TG-024 mandate related to the testing of protective footwear against the effect of AP blast mines.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".