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Record W2061639780 · doi:10.13031/2013.36495

Mental Workload Associated with Operating an Agricultural Sprayer: An Empirical Approach

2011· article· en· W2061639780 on OpenAlexafffund
A. K. Dey, Danny Mann

Bibliographic record

VenueJournal of Agricultural Safety and Health · 2011
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Manitoba
FundersUniversity of ManitobaNational Aeronautics and Space Administration
KeywordsWorkloadSimulationLatency (audio)Task (project management)Computer scienceEngineering

Abstract

fetched live from OpenAlex

Agricultural spraying involves two major tasks: guiding a sprayer in response to a GPS navigation device, and simultaneous monitoring of rear-attached booms under various illumination and terrain difficulty levels. The aim of the present study was to investigate the effect of illumination, task difficulty, and task level on the mental workload of an individual operating an agricultural sprayer in response to a commercial GPS lightbar, and to explore the sensitivity of the NASA-TLX and SSWAT subjective rating scales in discriminating the subjective experienced workload under various task, illumination, and difficulty levels. Mental workload was measured using performance measures (lateral root mean square error and reaction time), physiological measures (0.1 Hz power of HRV, latency of the P300 component of event-related potential, and eye-glance behavior), and two subjective rating scales (NASA-TLX and SSWAT). Sixteen male university students participated in this experiment, and a fixed-base high-fidelity agricultural tractor simulator was used to create a simulated spraying task. All performance measures, the P300 latency, and subjective rating scales showed a common trend that mental workload increased with the change in illumination from day to night, with task difficulty from low to high, and with task type from single to dual. The 0.1 Hz power of HRV contradicted the performance measures. Eye-glance data showed that under night illumination, participants spent more time looking at the lightbar for guidance information. A similar trend was observed with the change in task type from single to dual. Both subjective rating scales showed a common trend of increasing mental workload with the change in illumination, difficulty, and task levels. However, the SSWAT scale was more sensitive than the NASA-TLX scale. With the change in illumination, difficulty, and task levels, participants spent more mental resources to meet the increased task demand; hence, the illumination, task difficulty, and task level affected the mental workload of an agricultural sprayer operator operating a sprayer in response to a GPS lightbar.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.801

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.097
GPT teacher head0.381
Teacher spread0.285 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2011
Admission routes2
Has abstractyes

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