A Predictive Simulation Model of Regional Weather Events for Winter Road Maintenance Operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In construction field work, weather events are a key risk factor in planning. Such events can impact productivity and, ultimately, project progress. Unforeseen weather situations can lead to delays and budget overruns. Recent efforts have been made in the construction industry to collect weather data at variously located weather stations through sensor technology. These data provide a foundation for reliable weather information. Monitoring the weather situation in a given area still presents challenges, given that weather stations are located great distances apart. A scientific method is desired to make use of limited collected weather data in order to reasonably estimate detailed weather conditions for construction purposes. In this research, a simulation-based, mathematical model has been developed using kriging interpolation method to estimate weather situations and their corresponding impacts on construction projects. Weather data and geographical information are used as inputs for the proposed model. The weather situation in the project area is simulated based on historical data, and its impacts on project planning are analyzed using the proposed model. Based on this methodology, a case study on winter road maintenance is developed and presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it