Impact of Blue Force Tracking on Combat Identification Judgments
Bibliographic record
Abstract
OBJECTIVE: We examined the effectiveness of blue force tracking (BFT) decision support for dismounted infantry soldiers. BACKGROUND: Technologies to support combat identification (CID) are rapidly evolving and may be deployable to dismounted soldiers in the future. BFT systems are designed to mitigate the risk of fratricide by supplying positional information regarding friendly units to enhance situation awareness. METHOD: Participants played the role of a dismounted infantry soldier in a first-person perspective gaming environment and made engagement decisions for a series of simulated targets, half of which were enemies and half of which were friends. RESULTS: Participants performed better overall when they were able to use a BFT system than when they performed the task without assistance. When a 10-s latency was added to the updating of position information in the BFT, participants made significantly more false alarms (engaged a friendly target) regardless of whether they knew about the latency. CONCLUSION: The results indicate the promise of a personal BFT device to reduce the likelihood of fratricide by dismounted infantry soldiers. The results, however, also indicate that the effectiveness of such a device can be dramatically reduced when it does not provide real-time data. APPLICATION: Potential applications of this research include development of performance standards for BFT devices and assessment of decision support for dismounted soldiers.
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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 | 0.040 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".