A COMPUTER MODEL FOR SELECTING EQUIPMENT FOR EARTHMOVING OPERATIONS USING QUEUING THEORY
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 presents a computer model “FLSELECTOR” for equipment fleet selection for earthmoving operations. The methodology based on the queuing theory is incorporated in a computer module to account for the uncertainties in that are normally associated with the equipment selection process. FLSELECTOR is capable of assisting the users in making decisions required for earthmoving operations, such as determining the size and number of trucks and excavators, haul road lengths and surface conditions, etc…These decisions are based on the calculated output for all feasible fleets. An actual case study is presented in order to illustrate the effectiveness and performance of the FLSELECTOR in comparison with the simulation 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