RAPID SELECTING UAVs FOR COMBAT BASED ON THREE-WAY MULTIPLE ATTRIBUTE DECISION
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) can carry out more and more dangerous missions and strike deep in the skies over hostile military sites.Thus, selecting appropriate UAVs to attend combat through rapid assessment is a hot topic in current research.In consideration of formulating practical evaluation as a three-way multiple attribute decision making (MADM) problem, a comprehensive assessment method based on interval-valued intuitionistic fuzzy set (IVIFs) is introduced under the context of determining the precision combat mission.First, the critical attributes of the UAV combat effectiveness are determined according to battlefield intelligence.Second, the attribute weights are computed by exploring the feature information of attribute orders given by experts.Third, the conditional probability a UAV may be selected to fight is calculated in accordance with an improved IVIFs score function.Then the UAVs' classification results of three-way decisions are obtained by setting the risk avoidance coefficients.Finally, the validity of the given method is proved throughout the decision-making process of selecting UAVs in a combat mission.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".