Beyond Identity: Incorporating System Reliability Information Into an Automated Combat Identification System
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate display formats for an automated combat identification (CID) aid. BACKGROUND: Verbally informing users of automation reliability improves reliance on automated CID systems. A display can provide reliability information in real time. METHOD: We developed and tested four visual displays that showed both target identity and system reliability information. Display type (pie, random mesh) and display proximity (integrated, separated) of identity and reliability information were manipulated. In Experiment 1, participants used the displays while engaging targets in a simulated combat environment. In Experiment 2, participants briefly viewed still scenes from the simulation. RESULTS: Participants relied on the automation more appropriately with the integrated display than with the separated display. Participants using the random mesh display showed greater sensitivity than those using a pie chart. However, in Experiment 2, the sensitivity effects were limited to lower reliability levels. CONCLUSION: The integrated display format and the random mesh display were the most effective displays tested. APPLICATION: We recommend the use of the integrated format and a random mesh display to indicate identity and reliability information with an automated CID system.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".