Meaningful Level of Change in hybrid simulation for construction analysis
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
Hybrid models of System Dynamics (SD) and Discrete Event Simulation (DES) in the construction industry aim to provide decision makers with more accurate analysis. However, there are certain issues that can limit the applicability of SD-DES hybrid models for real construction job situations. Meaningful Level of Change (MLC) is a concept that has been proposed to prevent the time advancing issue in the hybrid models used within the construction domain. It is claimed that by utilizing the MLC, the running time of hybrid simulation models can be reduced while only slightly contributing to model inaccuracy. In this paper, we investigate the effects of utilizing the MLC for SD-DES hybrid models used for construction systems. First, the theoretical aspects of applying the MLC in hybrid models are investigated. Second, the effects of using different set values of MLC in an experimental model of a real construction system are illustrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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