Potential screening of photoremediating crops and performance of maize in photoremediated coal mined acid soil with phosphorus application
Bibliographic record
Abstract
Aim: To assess the ability of phosphorus to counteract harmful effects of heavy metals by reducing their concentration and increasing the maize yield. Methodology: Bulk surface soil sample (0-15 cm) was collected from heavy metal polluted soil of coal mine areas of Sutnga. Two pot experiments were conducted. Ten kg capacity pots were laid out for three phytoremediating crop and replicated 33 times. The processed soil was used for filling the pots. The first pot experiment was conducted to assess the phytoremediation efficiency of Helianthus annus and Vigna ungniculata on heavy metal polluted soil. Thereafter in the second pot experiment Zea mays L. was planted in pots maintained under experiment 1 and subsequently eleven levels of phosphorus i.e. 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 mg P kg-1 soil were super imposed in non-phytoremediated soil, phytoremediated soils through SSP. The elemental uptake and concentration of available phosphorus and heavy metal was determined.? Results: From the first pot culture experiment, it was observed that heavy metals content in soil after harvesting of phytoremediating crops reduced significantly in both the phytoremediated soils as compared to non-phytoremediated soil. From the second pot experiment it was observed that phosphorus as well as the heavy metals concentration and uptake in maize decreased significantly with increasing doses of phosphorus added in non phytoremediated soil.? Interpretation: This study clearly indicates that sunflower can be successfully grown as a phytoremediating crop in coal mine affected soils of Jaintia hills for phytoremediation of heavy metal polluted soils.
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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".