Location-aware real-time simulation framework for earthmoving projects using automated machine guidance
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
The cost-and-time-optimized planning of earthmoving projects has been significantly boosted as a result of deploying simulation techniques which enable project managers to effectively comprehend the behavior of projects. However, the realism and accuracy of the simulation models diminish as a result of the heavy reliance on the statistical data and of not taking into account the context-specific features of the project. Similarly, the more unique the characteristics of projects and novel the construction methods, the less the possibility of retrofitting a historic pattern to new projects. On the other hand, the identification of potential accidents on construction sites has been a major concern in the construction industry. To address these issues, this research proposes a framework based on the integration of new tracking technologies used in Automated Machine Guidance (AMG) with a real-time simulation technique. A prototype is developed to test and demonstrate the effectiveness of the proposed approach.
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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