Permafrost-Supported Linear Infrastructure Risk Analysis Software: Design and Goals
Bibliographic record
Abstract
Risk analysis has been used in non-permafrost regions as a decision-making tool to justify expenditures; however, the application of these techniques to permafrost infrastructure is limited. Risk for a danger (adverse event which causes infrastructure damage) is the product of hazard and consequence. The probability and costs of a danger’s occurrence is a hazard and the consequence, respectively. A computer program, created in the form of a Microsoft Excel macro and associated input spreadsheets, will calculate the risk for a section of permafrost-supported linear infrastructure, using statistical methods applied to limit state design criteria to determine hazards for common dangers, estimated direct costs for the repair of a hazard’s occurrence, and scaling factors to account for the indirect costs of damage to the infrastructure’s users and connected communities. Hazard calculations are based on geotechnical index property and climate variation using Monte Carlo simulation and first order second moment (FOSM) methods. Included within the program will be a climate change fragility analysis and a summation of the overall risk for the roadway section analyzed. Repeated analyses along the infrastructure can provide a section-by-section risk profile of the infrastructure and how this risk may change due to a warming climate. Results may be used as a decision-making tool for cost/benefit analyses to justify the use of adaptation methods, prioritize repair or reconstruction locations and monies, and plan for future conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".