Bibliographic record
Abstract
The current study was designed to explore alternative methods of enhancing the manner in which operators are alerted in the Halifax Class Frigate operations room. As the auditory modality is overloaded in the current alerting system, one method of potentially reducing perceptual overload is to replace auditory alerts with alerts presented in the visual domain. The purpose of the current study was to investigate how a high intensity task spread across multiple displays impacts the detection of visual alerts. The experimental design included two types of alerts (flashing border/status bar) presented independently on the left, right, or centre display or on all three displays. Participants were required to complete two tasks: 1. Classify and report contacts appearing on the centre display as hostile or neutral, and 2. Detect and respond to visual alerts. Reaction time to alerts and accuracy of the identification of alerts and contacts were examined. In general, reaction time to status bar alerts was faster than to border alerts, although no significant difference was observed when the alerts appeared on the left display. Responding to the status bar alert when it was presented on all three displays at once compared to all other alert configurations was found to be fastest. No significant difference in accuracy was found. Results in this study suggest that the type and location of visual alerts has a significant impact on reaction time but no impact on accuracy. Further investigation of the interaction between auditory and visual alerts and their impact on high intensity tasks is highly recommended for future work.
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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 | 0.016 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".