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Record W2002303908 · doi:10.1002/ajim.1067

Assignment of work involving farm tractors to children on North American farms†

2001· article· en· W2002303908 on OpenAlexaff
Barbara Marlenga, William Pickett, Richard L. Berg

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsQueen's University
FundersU.S. Public Health Service
KeywordsTractorWork (physics)MedicineDescriptive statisticsAgricultureOccupational safety and healthEnvironmental healthPopulationAgricultural machineryTelephone interviewHuman factors and ergonomicsPoison controlOperations managementAgricultural scienceEngineeringGeographyMathematicsStatisticsEnvironmental science

Abstract

fetched live from OpenAlex

BACKGROUND: Children are at high risk for tractor-related injury. The North American Guidelines for Children's Agricultural Tasks (NAGCAT) provide recommendations for the assignment of tractor work. This analysis describes tractor-related jobs assigned to farm children and compares them to NAGCAT. METHODS: A descriptive analysis was conducted of baseline data collected by telephone interview during a randomized, controlled trial. RESULTS: The study population consisted of 1,138 children who worked on 498 North American farms. A total of 2,389 farm jobs were reported and 456 (19.1%) involved operation of farm tractors. Leading types of tractor jobs were identified. Modest, yet important, percentages of children were assigned tractor work before the minimum ages recommended by NAGCAT. CONCLUSIONS: Children on farms are involved in tractor work at a young age and some are involved in jobs that they are unlikely to have the developmental abilities to perform. NAGCAT is a new parental resource that can be applied to these work situations.

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)0.000
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 score0.663
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.237
Teacher spread0.207 · 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

Citations38
Published2001
Admission routes1
Has abstractyes

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