The potentional of plants to cleanup metals from an old landfill site
Bibliographic record
Abstract
Old landfill sites contain different hazardous materials like heavy metals which have the ability to affects the entire environment. These places are sometimes covered by plants to increase the stability of the soil and to reduce the effects of erosion. 15 soil samples (3 samples from each place) and 5-7 timothy-grass (Phleum pretense) plants from 5 different places were taken from an old landfill place in an active landfill site in Högbytorp /Sweden owned by Ragn-sells Group Company. XRF scanning was used to analyze the metal content of soil samples and of plants. High concentrations of metals were detected in the soil samples like Fe with an average of about 25000 ppm, Mn about 250 ppm and 2800 ppm of Ti. The plants results showed an average concentration of Fe in the shoots about 730 ppm, Mn about 60 ppm and Ti about 1760 ppm. On the other hand, the roots results showed an average concentration of about 10 000 ppm of Fe, about 160 ppm of Mn and 2200 ppm of Ti. These results gave the indication that the Timothy-grass has the ability to extract metals from contaminated soils and can help to cleanup these soils.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".