The comparison of a revised Leopold matrix and fuzzy methods in environmental impact assessment, a case study: The construction of Al‐A'amiriya residential complex, Baghdad, Iraq
Bibliographic record
Abstract
Abstract Environmental impact assessment (EIA) is an efficient method to recognize and alleviate the unfavorable and inevitable impacts of human activity on the environment. The EIA is dependent on expert opinions, which can be influenced by personal experience and knowledge. To evaluate this influence, we carried out EIA of 6,000 residential apartment units of Al‐A'amiriya, Baghdad, Iraq, using a revised Leopold matrix and fuzzy methods. The matrix results reflected the direct opinions of experts. The fuzzy method was developed according to the principles of expert comments. In the matrix method, the environmental impacts of each activity were evaluated by scores between −5 (very bad) to 5 (very good). The inputs to the fuzzy method were intensity and weather stability. The fuzzy estimation of the environmental impact was composed of a group of three values; P1 as the intensity, P2 as the extent, and P3 as the persistence. We gathered the expert opinions and used the Analytical Hierarchical Process to determine the weighting coefficient of each fuzzy anticipation. The matrix method was dependent on the expert opinions. The mean values of the matrix and P‐values in each column and row scores depict the impacts of a project on the environmental aspects and each element of construction on the environment. The comparison of the results indicated an average difference of 30% between the Leopold matrix and fuzzy methods. In regard to the availability and sufficiency of data, the difference was around 10%.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".