A Rationale for Establishing Survivability Requirements for Objective Force Unmanned Army Platforms and Systems
Bibliographic record
Abstract
The introduction of high-tech equipment into the Army inventory has substantially increased battle effectiveness, reduced personnel requirements, and in some cases allowed the replacement of several manned operational platforms and systems with unmanned equivalents. The continued trend toward digital robotics in the battlespace has become extremely attractive to military planners, so much so that future warfighters are expected to employ a considerable number of unmanned platforms and systems. Up to this point in time, however, nuclear hardening criteria have been applied principally to manned systems and have been balanced to the nuclear survivability of the operating crew. This paper provides the rationale for establishing reasonable nuclear hardening criteria for objective force unmanned mission critical equipment. It starts with the survivability requirement and then identifies the process used to establish criteria for five unmanned equipment classes. Also included are the factors to be considered and the steps to be taken to establish hardening criteria for all nuclear weapons effects (NWE) and for all weapon yields of interest. The paper concludes with an application of the process to a hypothetical system. Details given in this paper form the basis for proposed Quadripartite Standardization Agreement (QSTAG) 2041, a standard for the Armies of the United States, the United Kingdom, Canada, and Australia.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".