User-centred information display design for tractor air seeder systems
Bibliographic record
Abstract
The ongoing technological developments in mobile agricultural machines (MAMs) for better control, efficiency, and performance demand better information processing capabilities at the user interface. When a technology-centered approach is used to design information displays, the quantity and quality of information presented to the user often exceeds the capability of the human operator for processing and using that information. This scenario causes excessive mental workload and reduces the situation awareness (SA) of the operator. This degraded SA of the operator can further degrade the performance of the MAM and compromise the safety of the operator. Using the goal directed task analysis (GDTA) approach, information needs of the operator can be better understood and information displays can be designed to better support the SA needs of the operator. This paper describes the detailed systematic GDTA of the tractor air seeder driving simulator (TAS-DS) system in the Agricultural Ergonomics Laboratory at the University of Manitoba to determine the information needs of the operator. Based on the information needs of the operator, a new display was conceptually designed to better support the SA needs of the operator. This newly designed display has yet to be evaluated to determine any SA and performance related benefits in comparison to the existing display.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".