Modeling Labor Productivity in Construction Projects using Hybrid SD-DES Approach
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Bibliographic record
Abstract
Labor productivity is one of the most significant factors in the evaluation of construction projects performance. Improvement of labor productivity is believed to have direct impact on outperformance of the project. This research argues about a hybrid SD-DES approach to model labor productivity considering the effects of both the context and operational level factors. The complex inter-related structure of different context factors affecting the labor productivity is modeled using system dynamics (SD) approach. Discrete event simulation (DES) is implemented to model the operational variables and their effects on labor productivity. Using the proposed hybrid SD-DES model, the labor productivity can be determined more precisely since the effects of both context and operational variables are taken into account. The proposed hybrid model is implemented in a real world case and the value of labor productivity is simulated considering the effects of both context and operational variables.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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