Situated Visual Alarm Displays Support Machine Fitness Assessment for Nonexplainable Automation
Bibliographic record
Abstract
Determine if situated visual alarm displays can support machine fitness assessment (MFA), facilitating improved hazard recognition and alarm accuracy assessment in the presence of inaccurate alarms. Poor performance of opaque automation is more difficult to detect, which increases the likelihood of cascades resulting in overall system failure. MFA reduces the negative impact of poor automation performance. Integrated alarm visualizations were shown to 32 nurses for 10 cases focused on patient outcome and 17 focused on alarm quality, all using real patient data. Five of the ten outcome cases would ultimately result in an emergency (unbeknownst to the nurse). Alarm cases ended with a true, false, or unnecessary alarm. Responses for nurses’ concern, confidence, alarm quality, and intended response were recorded. Qualitative analysis of interviews was performed. Using the situated visual alarm displays, nurses reported less confidence (6.5 vs. 9.1,ppppp< 0.001). Situated visual alarm displays combining visual trends with alarm signals improves detection of hazardous events and mitigates the negative effects of poor opaque automation performance.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".