Analysis of Heavy Metals in Azadirachta indica A. Juss Leaves, as Bioindicator for Monitoring Enviromental Pollution in Guayaquil, Ecuador
Bibliographic record
Abstract
In the Ecuadorian territory, there is no precise information of the air quality status. The only city that has an environmental monitoring network for certain pollutants is Quito, while in Guayaquil only atmospheric studies have been carried out between 1970 -1990 and 2003. Having a monitoring network in different cities is essential, but requires a lot of investment, it is for this reason that some countries have chosen to use plants as environmental bioindicators to assess the impact of a source of atmospheric pollution. Neem leaves can be used as bioindicators, bioaccumulators and biomonitors, due to their properties of accumulating high concentrations of metals. In the present study, heavy metals as Pb, As, Zn, Cd, Ni, Mn, Cr and Cu were analysed in leaves of Neem tree (Azaridachta indica A. Juss). The urban sampling areas selected were Plaza Coln, Av. Luis Vernaza and Loja, located in the city of Guayaquil-Ecuador. Spectrophotometric techniques as AAS and ICP were used for the determination of the metals on the leaves. The study demonstrated representative average concentrations for Zn> Cu> Mn> Ni> Cr in both sampling points. The values obtained for As, Pb and Cd were found below the limit of detection. These values indicated the existing environmental contamination in the selected urban areas due to vehicular traffic and demonstrate the efficacy of the Neem leaves as an environmental bioindicator.
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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) | 0.000 | 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".