Metal contents of some selected vegetables grown in Bodoland territorial region of Assam, India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it