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Record W2092444151 · doi:10.13031/2013.42542

Evaluation of Implement Monitoring Systems

2013· article· en· W2092444151 on OpenAlexafffund
Aadesh Kumar Rakhra, Danny Mann

Bibliographic record

VenueJournal of Agricultural Safety and Health · 2013
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPoison controlEngineeringForensic engineeringOccupational safety and healthHuman factors and ergonomicsInjury preventionComputer scienceReliability engineeringTransport engineeringMedical emergencyMedicine

Abstract

fetched live from OpenAlex

During monitoring of rear-mounted equipment, frequent rearward turning of tractor drivers causes awkward postures that can cause musculoskeletal disorders related to the back, neck, and shoulders. The objective of this study was to compare three implement monitoring strategies (direct viewing via physical turning, indirect viewing via rear-view mirrors, and indirect viewing via a camera-monitor system) in a lab environment using a tractor and air seeder driving simulator Comparison was based on monitoring performance of the operator (i.e., response error), physical impact on the operator (i.e., head/neck acceleration and increase in neck muscle temperature), and operator preference. Indirect viewing via a camera-monitor system caused the least physical impact on subjects and was the preferred implement monitoring strategy. No significant differences (alpha = 0.05) in monitoring performance were observed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.049
GPT teacher head0.324
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2013
Admission routes2
Has abstractyes

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