Environmental Risk of Turbidity Caused by Construction Activities near the Gulf in UAE
Bibliographic record
Abstract
Studies indicate that construction activities can affect several water surfaces; hence, the Environmental Protection Agency (EPA) has examined the environmental impacts of construction sites near water surfaces.However, whilst much attention has been given to environmental risks to streams and small rivers, there has been limited exploration of the risk to large bodies of surface water, especially in the Gulf.This research paper helps to address this shortcoming by focusing on turbidity caused by construction activities in two construction sites located twelve meters from the Gulf in Abu Dhabi City (the capital) in the United Arab Emirates (UAE) and at a 500m distance from each other.This distance is hypothesized as the safest distance for occupied buildings and recreation.The objective of the study is to monitor the environmental risk of turbidity near construction-booming areas and its impact on the nearby occupied residential area and recreation; and additionally, to assess the effect of temperature on turbidity levels at different times during the day.The first site was undergoing construction at varying levels ranging from excavation to structural framing and infrastructure activities.The second site was completed and occupied.Turbidity was monitored twice a week over a period of four weeks.Thirty-two water samples were tested during the peak time of construction activities, and after six hours (no activities), taking into consideration the weather conditions.The results indicate that maximum turbidity levels were noticed when the temperature was between 42°C and 44°C.This indication of turbidity during this time of the year may not be safe for any recreation adjacent to construction activities, especially since temperature increases the risks of the turbidity effect.Consequently, continuous monitoring is important.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".