Modified Cooper Harper Scales for Assessing Unmanned Vehicle Displays
Bibliographic record
Abstract
In unmanned vehicle (UV) operations, displays are often the only information link between operators and vehicles. It is essential these displays present information clearly and efficiently so that operators can interact with the UVs to achieve mission objectives. While there are a variety of metrics to evaluate displays, there is no current standardized methodology for operators to subjectively assess a display’s support and identify specific deficiencies. Such a methodology could improve current displays and ensure that displays under development support operator processes. This report presents a quasi- subjective display evaluation tool called the Modified Cooper-Harper for Unmanned Vehicle Displays (MCH-UVD) diagnosis tool. This tool, adapted from the Cooper-Harper aircraft handling scale, allows operators to assess a display, translating their judgments on potential display shortcomings into a number corresponding to a particular deficiency in operator support. The General MCH-UVD can be used to diagnose deficiencies for any UV display, while the Specific MCH-UVD is UV and mission specific in its evaluation of displays. This report presents the General MCH-UVD and provides guidance on how to adapt it to create a Specific MCH-UVD through the use of UV mission taxonomies and a questioning method. A UGV search mission case study provides a how-to guide example for generating a Specific MCH-UVD. The report also presents an experiment conducted to validate the MCH-UVD and assess if a mission-specific version is necessary, or if the general form of the MCH-UVD is sufficient for different UV display evaluation. The report concludes with discussion on how to administer the scale, when a Specific scale is necessary, MCH-UVD diagnosis tool limitations, and 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".