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Record W2014283891 · doi:10.5539/gjhs.v1n1p69

Opportunity Efficiency - Use Uncertainty Analysis to Evaluate Risks in Construction

2009· article· en· W2014283891 on OpenAlexvenueno aff
Hung Chun Hsu, Dongqi Jiang

Bibliographic record

VenueGlobal Journal of Health Science · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsExcavationInvestment (military)Risk analysis (engineering)HazardFrame (networking)Risk assessmentComputer scienceForensic engineeringEngineeringBusinessGeotechnical engineeringComputer security

Abstract

fetched live from OpenAlex

In central Taiwan, around the Taichung basin, the ground condition is boulders with red soils and high ground waterlevel. Local technicians have developed an unusual soil excavation method to build the so-called “soil retainingcolumns”. It is cheap, practical and highly efficient. However it is fraught with risk and uncertainty.In general, use of the injury severity method in occupational injury evaluation is a good solution. But, the method seemsunsuitable for high risk situation in construction sites. Traditionally, there are 3 excavation methods to frame the soilretaining piles. Their risk distributions are not similar. Thus, we can’t use the same safety investment budget when wechoose different excavation method. In this study, for increase the exactness of “risk quantity”, we focus on the different“risk distribution”. Based our risk evaluation of the “hazard uncertainty” concept and introducing the notion of“opportunity efficiency” to modify usage of the “risk severity” analysis; this concept will increase the accuracy ofsafety investment evaluations.

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)0.030
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.023
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.216
GPT teacher head0.495
Teacher spread0.279 · 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.

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

Citations0
Published2009
Admission routes1
Has abstractyes

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