Assessment of Soil Contamination through E-Waste Recycling Activities in Tema Community One
Bibliographic record
Abstract
The study investigated the level of heavy metal concentration in soils at e-waste recycling sites at Tema Community One. Two soil samples were collected from six different sites for laboratory analysis with a seventh location serving as a control. Heavy metals in soil samples were analyzed by digestion method and the use of atomic absorption spectrophotometer. The concentrations of Cadmium, Copper and Mercury were all higher at all the sites than those obtained for the control. The site that recorded higher concentration for copper was about 1200 times higher than the value for the control but statistically, there was significant difference between the concentrations of copper from the six sites (t = 5.168, p = 0.0036). Site 12 and GMG site had concentrations which is 5 times higher than the control and there was significant difference between the concentrations from the six sites and the control (t = 10.39, p = 0.0001) for the cadmium. The mean concentration of mercury from site 12 was found to be 34 times higher than the control value, however, there was no significant difference between the concentrations from the six e-waste recovery sites and the control (t = 2.593, p = 0.05194). E-waste recycling has contributed to the heavy metal contamination of the soil at the recovery sites. Workers safety in relation to these heavy metals is therefore worth researching in the future.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".