Charting the Edges of Human Performance
Bibliographic record
Abstract
In the Horizon 2020 funded Future Sky Safety programme, the Human Performance Envelope project pushed airline pilots to the edges of their performance in real-time cockpit simulations, by increasing stress and workload, and decreasing situation awareness. The aim was to find out how such factors interact, and to detect the edges of human performance where some form of automation support should be employed to ensure safe continued flight. A battery of measures was used, from behavioural to physiological (e.g. heart rate, eye tracking and pupil dilation), to monitoring pilot performance in real time. Several measures – e.g. heart rate, heart rate variability, eye tracking, cognitive walkthrough, and Human Machine Interface (HMI) usability analysis – proved to be useful and relatively robust in detecting performance degradation, and determining where changes in information presentation are required to better support pilot performance in challenging situations. These results led to proposed changes in a prototype future cockpit human-machine interface, which were subsequently validated in a final simulation. The results also informed the development of a ‘Smart-Vest’ that can be worn by pilots to monitor a range of signals linked to performance.
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 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".