Rough approximation-based random model for quarry location and stone materials transportation problem
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
This paper considers a bi-level multi-objective quarry location and stone materials transportation problem for a large-scale construction project with random phenomenon. Based on the characteristics and mechanisms of the problem, the objective functions and constraints are established. To deal with the problem uncertainty, an expected value operator is employed to deal with the random coefficients in the objective functions, and a rough approximation method is adopted to deal with the feasible region, which features constraints with random coefficients. Then a rough approximation-based bi-level multi-objective model is developed as an equivalent crisp model. To solve the complex and nonlinear bi-level multi-objective model, a hybrid genetic algorithm embedded with an interactive fuzzy programming technique is designed as a combined solution method. Finally, the results and a comparison analysis of a case study at the Xiluodu dam construction project are presented to demonstrate the practicality and efficiency of the optimization method.
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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