Design and Evaluation of Individual Elements of the Interface for an Agricultural Machine
Bibliographic record
Abstract
If a user-centered approach is not used to design information displays, the quantity and quality of information presented to the user may not match the needs of the user, or it may exceed the capability of the human operator for processing and using that information. The result may be an excessive mental workload and reduced situation awareness of the operator, which can negatively affect the machine performance and operational outcomes. The increasing use of technology in agricultural machines may expose the human operator to excessive and undesirable information if the operator's information needs and information processing capabilities are ignored. In this study, a user-centered approach was used to design specific interface elements for an agricultural air seeder. Designs of the interface elements were evaluated in a laboratory environment by developing high-fidelity prototypes. Evaluations of the user interface elements yielded significant improvement in situation awareness (up to 11%; overall mean difference = 5.0 (4.8%), 95% CI (6.4728, 3.5939), p 0.0001). Mental workload was reduced by up to 19.7% (overall mean difference = -5.2 (-7.9%), n = 30, a = 0.05). Study participants rated the overall performance of the newly designed user-centered interface elements higher in comparison to the previous designs (overall mean difference = 27.3 (189.8%), 99% CI (35.150, 19.384), p 0.0001.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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 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".