Using synthetic climate datasets for geotechnical and geoenvironmental design problems
Bibliographic record
Abstract
Location-specific climate datasets are required for the design and evaluation of a number of civil engineering projects. It requires huge effort to compile a multi-year quality-controlled climate dataset. In this paper, a method of generating simulated daily climate variables of interest from readily available climate normal using the general-purpose weather generator SIMETAW is presented. The accuracy of this method is assessed by comparing the climate datasets generated using SIMETAW with the recorded historical climate datasets for nine different sites across Canada with climates ranging from semi-arid to pre-humid. This comparison was done using visual presentations as well as statistical analyses of the two datasets. It was found that the multi-year daily climate datasets generated by SIMETAW using just 12 monthly climate normal values are fairly similar to the recorded historical climate datasets. The usefulness of SIMETAW-generated climate datasets was demonstrated by using them in numerical simulations of three different design problems, namely, infiltration into soils, swelling potential of an expansive soil, and soil cover design. From the results of these numerical simulations, it is concluded that the SIMETAW-generated multi-year daily climate datasets are satisfactory for use in the geotechnical and geoenvironmental problems of the kind simulated herein.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".