Study the urban soils contamination by heavy metals for selected industrial locations in the Greater Toronto area, Canada, using multivariate statistical analysis
Bibliographic record
Abstract
A good understanding of urban soils metal contamination and locating their pollution sources due to industrialization and urbanization is important for addressing environmental problems.Urban soil samples near industrial locations in the Greater Toronto Area (GTA), Ontario, Canada were analyzed for metal (Cr, Mn, Fe, Ni, Cu, Zn, and Pb) contamination.Multivariate geo-statistical analysis (correlation matrix, cluster analysis, principal component analysis) was used to estimate the variability of the soil chemical content.The correlation matrix exhibits a negative correlation with Cr.The principal component analysis (PCA) displays two components.The first component explains the major part of the total variance and is loaded heavily with Cr, Mn, Fe, Zn, and Pb, and the sources are industrial activities and traffic flows.The second component is loaded with Ni, and Cd, and the sources could be lithology and traffic flow.The results of the cluster analysis demonstrate three major clusters: 1) Mn-Zn, 2) Pb-Cd-Cu and Cr, 3) Fe-Ni.The geo-accumulation index (I geo ) and the pollution load index (PLI) were determined and show the main I geo values to be in the range of 0-1.67, indicating the studied soil samples are slightly to moderately contaminated with Cr, Fe, Cu, Zn, and Cd, and moderately contaminated with Pb, while Ni, and Mn fall into class "0".Regarding the PLI, the lowest values are observed at stations 6, 7, 9, 10, 11, 12, 25, 27 and 28, while the highest values are recorded for stations 1, 5, 6, 13, 14, 16, 17, 18, 20, 22 and 24, and very high PLI readings are seen for stations 5, 13, 16, 17, 18, 22 and 24.These data confirm that in addition to heavy traffic flows, the chemical and metallurgical type of industries are the major source for soil pollution in the GTA.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".