Freshwater Phenanthrene Removal by Three Emergent Wetland Plants
Bibliographic record
Abstract
The use of floating wetlands has been receiving increased attention as a minimally invasive method for oil spill remediation, but the species of vegetation incorporated in floating wetlands may influence the success of oil degradation. Therefore, a freshwater microcosm experiment was conducted at the IISD Experimental Lakes Area, Canada to assess the potential of common wetland plants Typha sp., Carex utriculata, and C. lasiocarpa, to remove phenanthrene, a polycyclic aromatic hydrocarbon ubiquitously found at oil spill sites. Triplicate microcosms containing 3L of lake water were established with either Typha sp., Carex utriculata, or C. lasiocarpa and then treated with nominal concentration of 1 mg/L phenanthrene and monitored over 21 days. Two types of reference microcosms were also included: one set with the same plant allocations but not treated with phenanthrene and another with water only and no plants or phenanthrene. Phenanthrene declined by over 89.30% in all microcosms that received the compound, but the decline was more rapid in microcosms that included Typha sp. and C. lasiocarpa, than those with C. utriculate or no plants. Declining phenanthrene concentrations in microcosms without plants may have resulted from biofilm stimulation. Specific conductivity and pH were influenced by plant type but not phenanthrene, while dissolved oxygen was influenced by both. There was no influence of phenanthrene on plant growth rates or root biofilm bioactivity, measured by adenosine triphosphate or oxygen consumption. Results indicate there may be plant-specific factors influencing remediation success which should be explored in future research.
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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".