Phytoremediation of biosolids from an end-of-life municipal lagoon using cattail (<i>Typha latifolia</i>L.) and switchgrass (<i>Panicum virgatum</i>L.)
Bibliographic record
Abstract
Land spreading of biosolids as a disposal option is expensive and can disperse pathogens and contaminants in the environment. This growth room study examined phytoremediation using switchgrass (Panicum virgatum L.) and cattail (Typha latifolia L.) as an alternative to land spreading of biosolids. Seedlings were transplanted into pots containing 3.9 kg of biosolids (dry wt.). Aboveground biomass (AGB) was harvested either once or twice during each 90-day growth period. Switchgrass AGB yield was greater with two harvests than with one harvest during the first 90-day growth period, whereas cattail yield was not affected by harvest frequency. In the second growth period, harvesting frequency did not affect the yield of either plant species. However, repeated harvesting significantly improved nitrogen (N) and phosphorus (P) uptake by both plants in the first period. Phytoextraction of P was significantly greater for switchgrass (3.9% of initial biosolids P content) than for cattail (2.8%), while plant species did not have a significant effect on N phytoextraction. The trace element accumulation in the AGB of both plant species was negligible. Phytoextraction rates attained in this study suggest that phytoremediation can effectively remove P from biosolids and offers a potentially viable alternative to the disposal of biosolids on agricultural land.
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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".