Germination Potential of Six Native Plant Species for Phytoremediation of Hydrocarbon Contaminated Peat Soils
Bibliographic record
Abstract
Research on the remediation of hydrocarbon contaminated peatlands is limited; in particular, hydrocarbon effects on seed germination is critical for effective reclamation. This study examined germination responses of six wetland plant species under greenhouse and laboratory conditions. Seeds were exposed to hydrocarbon-contaminated peat soil and ground water under two light treatments (light, total darkness) for four weeks. Species specific responses in seed germination and germination velocity occurred under different light conditions and exposure to hydrocarbon-contaminated peat soil and water. Light significantly impacted germination, while hydrocarbon-contaminated peat soil and water had no effect. Glyceria grandis (83.5%) and Scirpus microcarpus (74%) had significantly higher germination rates even in contaminated treatments than Carex aquatilis (28%) and Typha latifolia (38%), which had modest germination. Modified Timson’s Index (germination velocity) was significantly greater in Scirpus microcarpus (21.90) and Glyceria grandis (19.20) than in other species after 30 days. Carex utriculata and Scirpus validus had ≤0.5% germination and ≤0.2 velocity. The overall species mean germination time was >9 days with a low (≤0.7) germination index. Ordination using several germination variables separated some species. These findings suggest Scirpus microcarpus and Glyceria grandis have high tolerance to hydrocarbon contamination and may be effective candidates for the phytoremediation and restoration of contaminated peatlands.
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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) | 0.000 | 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 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".