Evaluation of theory and practices for assessing local environmental impacts in construction projects
Bibliographic record
Abstract
An Environmental Impact Assessment (EIA) is a planning and decision-making tool utilized to assess the potential effects of construction projects on the environment. Environmental aspects are categorized into nine domains, one of which is local issues. This category encompasses noise, vibration, dust, odor, visual appearance impacts, etc. Risks of construction activities on human health, wildlife habitats, and the environment can be reduced by the early identification of the environmental issues, their sources, and receptors, along with the implementation of mitigation measures. This paper seeks to evaluate the current theories and practices employed in assessing local issues within construction projects, considering the main aspects of local impact assessment. Standardized protocols for comprehensively analyzing local issues in construction projects are lacking, while research works focus on developing sophisticated management strategies and simulation techniques to predict and mitigate local issues. Therefore, there is a need for practical and user-friendly impact simulation tools that allow full environmental assessments, such as four-dimensional Building Information Modeling (4D BIM) platforms and techniques.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".