Lessons learned from utilizing discrete-event simulation modeling for quantifying construction emissions in pre-planning phase
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
Construction operations have a tremendous impact upon both the environment and public health due to the generation of significant amounts of airborne emissions, including greenhouse gases and other traditional criteria air pollutants. Quantifying emissions in the pre-planning phase of construction operations is the first step in identifying mitigation opportunities. The authors therefore have quantified construction emissions produced by various types of construction operations through the use of discrete-event simulation (DES). The paper focuses upon the utilization of DES in various case studies and delineates the lessons learned. An overview of each case project is provided, the benefits and limitations of DES are identified, and means to mitigate these limitations are discussed. The lessons learned from the case studies utilized in the paper are helpful; simulation practitioners and researchers can exploit these studies in simulation models that examine the environmental aspects of construction operations.
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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) | 0.000 | 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