Metal contents of some selected vegetables grown in Bodoland territorial region of Assam, India
Bibliographic record
Abstract
Metals play a crucial role in the metabolic pathways during the growth of vegetable plants. The presence of heavy metals or trace metals also takes a vital role in the nutrient quality of a vegetable. The vegetables are an inevitable part of the human diet and provide essential nutrients to maintain the normal functioning of human health and growth. The application of fertilizers and pesticides facilitates the accumulation of heavy metals by the vegetables grown in the fields. Consumption of heavy metals beyond the permissible limit along with vegetables may impact human health. Moreover, the production of nutritious food and its safety is an important aspect of the measure of any nation’s economy. Considering all these points, the present work was undertaken to analyze the heavy metal contents in the six mostly produced and consumed vegetables grown in Bodoland Territorial Region (BTR), a tribal-dominated region of the state Assam, India. The vegetables analyzed were fern leaves (Diplazium esculentum), jute leaves (Corchorus olitorius), green arum leaves (Colocasia esculenta), pointed gourd (Trichosanthes dioica), yard long bean (Vigna unguiculata ssp. Sesquipedalis) and spiny gourd (Momordica dioica). The metals analyzed were Cu, Fe, Ni, and Zn. The presence of heavy metals was detected in all the vegetable 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 | 0.001 | 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".