Opportunity Efficiency - Use Uncertainty Analysis to Evaluate Risks in Construction
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.023 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".