Bibliographic record
Abstract
The work documented herein is a portion of a multi-organizational effort lead by the University of Missouri-Rolla (UMR) Rock Mechanics and Explosives Research Center, with the following participants: Kontek Industries, the Air Force Research Laboratory Airbase Technologies Division, UMR's Department of Civil, Architectural, and Environmental Engineering, the Department of Architectural Studies from the University of Missouri-Columbia (UMC), and UMC's National Center for Explosion Resistant Design. The ultimate goal of this multi-year project is to establish prototypical functionality and architectural standards for blast-resistant barricade systems through applied research, design, and test efforts. This paper specifically addresses the results of efforts by UMR and Kontek to design and test barrier structures that protect other structures, and mitigate pressure loads and shock hydrodynamic effects on structural barriers, columns, beams, and bents. As a part of this effort, we are examining structural load path transfer during a blast, in order to provide additional support to portions of structure under attack. Continuation of the current close coordination among the authors in the areas of analytical modeling and blast design and test, as well as the commercial constructability, allows the design of mid-scale and full-scale experiments to populate and validate empirical models for blast barriers, to include off-axis pressure prediction and the development of empirical-based algorithm for prediction of blast pressures around structures and barriers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".