Effects of Urbanization on the Heavy Metal Concentrations in the Red Sea Coastal Sediments, Egypt
Bibliographic record
Abstract
The total concentrations of heavy metals (Cu, Zn, Pb, Cd, Fe, Mn, Ni and Co) were determined in surface sediments from the coastal area of the Red Sea in four cities (Ras-Gharib, Hurghada, Safaga, and Qusier). In all sediment samples, the mean concentration ranges in (µg/g) of the studied metals were 11.2-145.3, 14.2-225.5, 18.5-90.8, 1.4-5.6, 1373-31,089, 72.5-758.5, 15.3-65.7 and 10.2-26.3, respectively. The effects of population pressure and different activities on metal contamination were evaluated, and metals were grouped according to sources of contamination using Principal Component Analysis (PCA). Maritime activities in Hurghada showed highest risk of contamination with Cu, Zn and Pb, while the sediments of Safaga City showed highest contaminated with Fe and Mn. The sediments quality and ecological risk of heavy metals were assessed relating to the sediments background levels of metals and calculating contamination factor (CF), metal pollution load index (MPI), enrichment factor (EF) and geo-accumulation index (I geo ). Average values of EF showed that Pb and Cd were highly enriched from anthropogenic contamination. The recorded (I geo ) values of Co and Cd were categorized as moderately polluted, while Pb was strongly effective pollutant in the studied sediments.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".