Spatial Distribution of some Important Heavy Metals in the Soils South of Manzala Lake in Bahr El-Baqar Region, Egypt
Bibliographic record
Abstract
The present work attempts to establish the distribution of Iron, Copper, Cobalt, Nickel, Zinc, Lead, Cadmium and Chromium in the soils of Bahr EL-Baqar Region. Where, eight soil samples were collected from Bahr EL-Baqar Region, South of EL-Manzala Lake. Elements (Metals) concentrations in the soils were varied between 11987.67-33567.43; 62.22-270.20 ; 74.6-106.44 ; 54.29-80.30; 95.13-211.22 ; 33.73-54.40; 12.22-19.39; and 96.76-144.55 mg/kg for Fe, Cu, Co Ni, Zn, Pb, Cd and Cr respectively. The abundance of heavy metals measured in these soils decreases as follows: Fe > Zn > Cr > Cu > Co > Ni > Pb > Cd. The heavy metals concentrations of Fe, Cu , Co, Ni, Zn, Pb, Cd and Cr from the soil samples of Bahr EL-Baqar region compared with Canadian soil quality guidelines (CSQG) of Canadian Council of Ministers of the Environment.(CCME), (2007) and European Union Standards (EU,2002) as well as with average upper earth crust of Wedepohl (1995). Another assessment method was applied using certain indices to assess the environmental impacts of the heavy metal pollution of the soils of Bahr EL-Baqar Region. These indices include the Enrichment Factor, Contamination Factor, Pollution Load Index and Degree of Contamination. The most important heavy metals with regards to potential hazards in studied soils are Cu, Pb and Cd. Keywords : Bahr EL-Baqar – Heavy Metals – Pollution - Indices Calculations – Guidelines
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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 | 0.000 |
| 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".