Heavy Metals in Leaf, Stem Bark of Neem Tree (Azadirachta indica) and Roadside Dust in Maiduguri Metropolis, Borno State, Nigeria
Bibliographic record
Abstract
Street dust and neem tree samples (Azadirachta indica) from Maiduguri Metropolis, Borno State, Nigeria were collected for the determination of trace elements. The highest concentrations of metals were found to be higher at the seven sampling points, while the lowest levels were observed in the street dust samples from the control sites. The concentrations of all the metals in plant samples were significantly highest in the leaves of Azadirachta indica, while the stem bark shows the least values. Levels of chromium (Cr), lead (Pb), nickel (Ni), cobalt (Co), cadmium (Cd) and arsenic (As) in plant samples exceeded the world health organization standard limits for medicinal plants. At the same time, the traffic situation in the area of study might be regarded as a source of heavy metal content in the roadside dust and plant samples.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".