Bibliographic record
Abstract
Flightcrew’s responses to alerts save lives multiple times a day. The proper design of the alert ensures correct detection, interpretation, and timely response. This paper explores the many factors of successful flightcrew alerting through the history, human factors research, regulations, and successes. By the 1970s transport aircraft had become complex, resulting in an increase in alert states (e.g., Boeing 747 with 455 alerts). Research revealed that the lack of prioritization, differentiation, and aggregation of flight deck alerts was a safety issue. The qualities of effective alerting were known: quickly orient, explain action needed, convey priority, minimize false positives/negatives, and indicate adequacy of resolution. In 1981, based on their human factors research, three large transport aircraft manufacturers compiled voluntary standards for alerting systems for the next generation of transport aircraft. Regulations and advisory materials followed for effective and standardized alerting (e.g., EASA CS 25.1322). These standards define the priority and appearance of warnings, cautions, and advisories. To comply, aircraft must have integrated alerting systems with complex logic, centralized data busses, sensors, and displays. Alerts for external hazards such as terrain, windshear, and traffic must also be integrated. These regulations continue to be harmonized across regulatory agencies. Guided by standardization, many human factors issues apply, including appropriate sensory modality, task saturation, crew coordination, and basic user-interface principles. Human factors science advises on what to alert, when to alert, where to alert, and how to alert effectively. All these factors are explained here. The relative criticality balanced with urgency leads to the priority of any alert being properly activated, given the current conditions. Even critical alerts are inhibited during high-workload moments in the flight. The flight deck systems must promote accurate flight crew response and immediate feedback when the non-normal condition no longer exists. The ability to reduce distraction by suppressing an alert is also important but must be accompanied by a salient indication that it has been suppressed. Naturally, nuisance occurrences reduce the effectiveness of alerts and must be minimized. Proper alerting requires continual improvement. Our human factors work is not complete. Flight data and incident/accident reports are an important source of alerting successes and failures. These data show us that alerts fail to be properly activated, properly responded to, and properly trained. Two areas that show promise in this area are training for response to the startle effect and to loss of control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".