Utilizing simulation derived quantitative formulas for accurate excavator Hauler fleet selection
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
Discrete event simulation (DES) produces models of greater granularity and higher accuracy in analysis of heavy construction operations than classic quantitative techniques; specifically utilizing average production rates for determining the fleet required for and duration of earthmoving operations. Nonetheless, the application of DES is not readily applied beyond academic work for high level analysis in the heavy construction industry. Field level planners default to the use of average production rates, which can be easily applied with simple spreadsheet tools and allows quick recalculations to be performed when existing input data is changed or more data becomes available. To aid in fleet selection and determination of the duration of site grading earthworks operations where one fleet is applied, this research presents a new approach by developing quantitative formulas from DES analysis. The approach simplifies DES application and reduces the barrier to access simulation-generalized and field-applicable knowledge, while providing greater accuracy than simply relying on average production rates.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it