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Record W1991371111 · doi:10.1080/00140139.2013.824615

Wrist postures and forces in tree planters during three tree unloading conditions

2013· article· en· W1991371111 on OpenAlexaff
Kathleen Denbeigh, Tegan Slot, Geneviève Dumas

Bibliographic record

VenueErgonomics · 2013
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsWristShovelCarpal tunnel syndromeMedicineWrist painPhysical medicine and rehabilitationBiomechanicsPhysical therapyOrthodonticsAnatomyEngineeringSurgeryMechanical engineering

Abstract

fetched live from OpenAlex

The aims of this study were to investigate wrist postures and forces while operating the shovel during tree planting and to determine if different tree unloading techniques result in variations in wrist postures and forces. Experienced tree planters performed the planting task in a laboratory environment for three conditions: (1) symmetric tree unloading, and asymmetric unloading resulting in (2) right-loaded planting bags and (3) left-loaded planting bags. An optoelectric system and a shovel instrumented with strain gauges captured wrist posture and forces at the wrist, respectively. Wrist extension of up to 45° was observed, and this posture, in combination with varying degrees of wrist deviation, may be a primary risk factor for musculoskeletal pain. Average resultant forces at the wrist were moderately high (>30 N) for each unloading condition, indicating increased risk for the development of repetitive strain injuries such as carpal tunnel syndrome. No significant differences in wrist posture or forces existed between unloading conditions. PRACTITIONER SUMMARY: Wrist pain is a major musculoskeletal complaint among tree planters. This study measured wrist postures and forces at the wrist while operating the shovel during tree planting. The wrist extension observed, in combination with deviation, may be a key risk factor for musculoskeletal pain. Forces at the wrist indicate increased risk for repetitive strain injuries.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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