Saliz discolor: Prospects for phytoremediation of lead and polycyclic aromatic hydrocarbons
Bibliographic record
Abstract
Members of the genus Salix, more commonly known as willows, are of great interest in the field of phytoremediation. Their ability to rapidly accumulate biomass, grow in disturbed conditions, aggressively seek water and development of extensive root systems are well documented – all highly desirable traits for phytoremediation, yet the remediation capabilities of most species remains unknown. The present study seeks to determine the ability of Salix discolor to remediate lead and polycyclic aromatic hydrocarbons (PAHs) – two of the most common pollutants in urban and industrial areas and which are often found in states of co-contamination. The ability of S. discolor to remove these contaminants from aqueous media was tested using a hydroponic experiment. The treatments consisted of an uncontaminated control, lead or PAH contamination and lead and PAH co-contamination. The concentration of the media was monitored weekly, and at the conclusion of the experiment the accumulation of the lead and PAHs in the roots, shoots and leaves of the plants will be analyzed using atomic absorption spectrometry and gas chromatography – mass spectrometry, respectively. Additionally, biomass accumulation will be recorded to determine the effects of contamination on the growth of the plants. Results are pending until the experiment has concluded. *Indicates faculty mentor.
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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) | 0.002 | 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".