A Video Prototyping Methodology for Evaluating Novel Interface Concepts in Cockpit Displays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern cockpit displays contain a multitude of complex information sources. Integrating new interface concepts into an existing cockpit display to produce a high-fidelity prototype suitable for user testing can be extremely time-consuming. Discount prototyping methodologies are needed to enable user testing at earlier stages of the design cycle to ensure appropriate changes occur and high quality interfaces result. Video prototyping can provide a useful step between low-fidelity, static prototypes and higher-fidelity software prototypes. However, existing video prototyping methods are designed to elicit user feedback on design concepts. While user feedback is important to the adoption of aviation interfaces, it is also desirable to examine performance using more complex metrics, which have traditionally required the development of a fully interactive software prototype. We propose a new scenario-driven video prototyping methodology that allows designers to apply complex metrics during early-stage user evaluations.
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.
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 | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it