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Record W1985305591 · doi:10.2118/86626-ms

Ergonomic Considerations in Physically Demanding Jobs

2004· article· en· W1985305591 on OpenAlexaff
Dennis Attwood, Dave Fennell

Bibliographic record

VenueSPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production · 2004
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsImperial Oil (Canada)
Fundersnot available
KeywordsInterimComputer scienceWork (physics)Operator (biology)Field (mathematics)Risk analysis (engineering)Industrial engineeringComputer securitySimulationEngineeringBusinessMechanical engineeringLaw

Abstract

fetched live from OpenAlex

Abstract Within the oil and gas industry, some physical jobs haven't changed for years. Even today, many of the tasks performed on-site and in the field are physically demanding - so much so that many tasks exceed the long-term physical capability of most operators. Over time, we have seen workers gradually ‘wear out' physically and self-elect to move into tasks that are less demanding, but also less rewarding. In our opinion, it does not have to be this way. The objective of this paper is to demonstrate how physically demanding jobs can be analyzed and how practical, cost-effective changes can be made to reduce the physical stress on the operator. It is our goal to demonstrate how positions that are now seen as ‘interim work' can be made into careers that workers can perform into their retirement.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.128
GPT teacher head0.403
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

Citations1
Published2004
Admission routes1
Has abstractyes

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