Elemental Contents of Spinach and Lettuce from Irrigated Gardens in Kano, Nigeria
Bibliographic record
Abstract
One way analysis (ANOVA) was used to analyze a large dataset of elemental levels of two vegetables – spinach (Amaranthus cruentus) and lettuce (Lactuca sativa) grown around River Jakara in Kano, Nigeria using data generated during 12 months of monitoring Ca, K, Mg, Na, (essential bulk elements) Cu, Zn, Cd, Ni, Cr, Co, Pb and Fe (trace/heavy elements) concentrations collected at three designated sites. The concentrations of the elements showed insignificant differences between sites but significant differences between some months. The soil was implicated as the major source of the elements. The concentrations of the trace/heavy metals exceeded those of the international permissible limits which pointed to the contamination of the vegetables. The mean concentrations of the elements occurred in the magnitude of Ca > Mg > K > Na > Fe > Zn > Pb > Co > Cr > Cu > Ni > Cd and Ca > Na > K >Mg > Fe > Zn > Pb > Cr > Co > Cu > Ni > Cd in the spinach and lettuce respectively. The continued consumption of these vegetables by the inhabitants of Kano and its environs present a public health risk with regards to their concentrations with heavy metals. It is therefore recommended that the relevant organ of government should find an alternative farmland for the farmers within the catchment area of River Jakara where unpolluted soil can be utilized for the production of the vegetables.
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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) | 0.001 | 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".