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Record W4213383366 · doi:10.4236/as.2022.132016

Design and Evaluation of a User Interface for an Autonomous Agricultural Sprayer

2022· article· en· W4213383366 on OpenAlex
Uduak Edet, Franklin Ogidi, D. D. Mann

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAgricultural Sciences · 2022
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of SaskatchewanUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSprayerInterface (matter)Human–computer interactionAutomationComputer scienceUser interfaceUsabilityField (mathematics)EngineeringOperating system

Abstract

fetched live from OpenAlex

This paper describes the design and evaluation of a user interface for a remotely supervised autonomous agricultural sprayer. The interface was designed to help the remote supervisor to instruct the autonomous sprayer to commence operation, monitor the status of the sprayer and its operation in the field, and intervene when needed (i.e., to stop or shut down). Design principles and guidelines were carefully selected to help develop a human-centered automation interface. Evaluation of the interface using a combination of heuristic, cognitive walkthrough, and user testing techniques revealed several strengths of the design as well as areas that needed further improvement. Overall, this paper provides guidelines that will assist other researchers to develop an ergonomic user interface for a fully autonomous agricultural machine.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.115
GPT teacher head0.412
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it