Layered autonomous overwatch: the necessity and feasability of multiple unmanned systems in combat support
Bibliographic record
Abstract
Unmanned systems simultaneously reduce risk and magnify the impact of soldier-operators. For example, in Afghanistan UAVs routinely provide overwatch to manned units while UGVs support IED identification and disposal roles. Expanding these roles requires greater autonomy with a coherent unmanned "system of systems" approach that leverages one platform's strengths against the weakness of another. Specific collaborative unmanned systems such as shared sensing, communication relay, and distributed computing to achieve greater autonomy are often presented as possible solutions. By surveying currently deployed systems, this paper shows that the spectrum of air and ground systems provide an important mixture of range, speed, payload, and endurance with significant implications on mission structure. Rather than proposing UxV teams collaborating towards specific autonomous capabilities, this paper proposes that basic physical and environmental constraints will drive tactics towards a layered, unmanned battlespace that provides force protection and reconnaissance in depth to a manned core.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".