Ability of Cold-Tolerant Plants to Grow in Hydrocarbon-Contaminated Soil
Bibliographic record
Abstract
Phytoremediation of hydrocarbons in soil involves plants and their associated microorganisms. Differences in environmental conditions and restrictions on species importation mean that each country may need to identify indigenous plants to use for phytoremedation. Screening plants for hydrocarbon tolerance before screening for degradation ability may prove more economical than screening directly for degradation. Thirty-nine cold-tolerant plants native, or exotic and naturalized, in western Canada were assessed for their ability to survive in crude oil-contaminated soil. Four naturalized grasses (i.e., Agropyron pectiniforme, Bromus inermis, Phleum pratense, and Poa pratensis), three naturalized legumes (i.e., Medicago sativa, Melilotus officinalis, and Trifolium repens), two native forbs (i.e., Artemisia frigida and Potentilla pensylvanica), one native grass (i.e., Bromus ciliatus) and two native legumes (i.e., Glycyrrhiza lepidota and Psoralea esculenta) exhibited phytoremediation potential, based on survival. We determined the effect of increasing crude oil concentrations on total and root biomass, and relative growth rate of those species with the highest survival. The addition of 0.5%, 1%, and 5% (crude oil wt/fresh soil wt) crude oil to soil significantly decreased both the total biomass by at least 22% of the control and the relative growth rate of all species except P. esculenta. Root biomass significantly decreased by at least 22% with crude oil addition in all species except P. esculenta and A. frigida. Total biomass production in contaminated soil had a significant negative correlation with the relative growth rate in uncontaminated soil.
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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".