Effects of Some Heavy Metals on Seed Germination Characteristics of Canola (Barassica napus), Wheat (Triticum aestivum) and Safflower (Carthamus tinctorious) to Evaluate Phytoremediation Potential of These Crops
Bibliographic record
Abstract
Heavy metal pollutants are the main concern of new agricultural productions. Industrial products and using synthetic materials leads to drastically increase in concentration of different heavy metals in the environment. Lead and Cadmium are two famous heavy metals which are largely used in electronic industries thus the waste water of factories could pollute agricultural lands. Different heavy metal solutions were investigated for their effects on seed germination characteristics and phytoremediation potential of two oil crops ( canola, Brassica napusandsafflower, Carthamustinctorious) and a cereal crop (wheat, Triticumaestivum). The Canola, Safflower and Wheat seeds were germinated after treatments in solutions containing varying concentrations of heavy metals. Five different concentrations of heavy metals including (BiNO3, BiNO3, CdNO3, Sr (NO3)2, ZnNO3) at 50, 200, 350, 500, 1000 ppm and distilled water considered as control treatment. Results showed that in all treatments the percentage of seed germination, root and shoot length decreased as concentrations of solution increased. No germination was observed at 1000 ppm of cadmium level. Root and seedling vigor increased by application of 200 ppm of BiNO3. There were no seedling growth at 350 and 500 ppm of cadmium and lead concentration.
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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 | 0.000 | 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) | 0.001 | 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".