Nutrient Supply Rates and Phytoextraction during Wetland Phytoremediation of an End‐of‐Life Municipal Lagoon
Bibliographic record
Abstract
Core Ideas Nitrogen supply rate did not vary with time in June and July. Nitrogen supply rate increased with time after July. Phosphate supply rate remained relatively unchanged during the sampling period. Cumulative nutrient supply rate was positively correlated with plant uptake. In situ phytoremediation of municipal biosolids is a promising alternative to land spreading and landfilling during decommissioning of end‐of‐life municipal lagoons. Plant root simulator (PRS) probes can be used to examine nutrient availability during phytoremediation, but their use under wetland conditions is limited. This study examined nutrient availability using PRS probes during phytoremediation of biosolids vegetated with cattail. The probes were buried in the sediment for seven sequential 2‐wk burial periods beginning in June 2014. Plants were harvested to determine biomass yield and nutrient content. Nitrogen supply rate did not change significantly with sampling period in June and July (4.5 to 5.9 μg cm –2 [2 wk] –1 ) but increased thereafter to 11.8 μg cm –2 (2 wk) –1 . Phosphate supply rate (20.5 to 24.2 μg cm –2 [2 wk] –1 ) did not differ significantly among sampling times. Cumulative supply rates of the macronutrients N, P, K, Ca and Mg ( r = 0.77–0.92) and the micronutrients B, Fe, and Mn (r = 0.7–0.81) were highly correlated with cattail uptake, while the correlation was weaker for Cu ( r = 0.42) and Zn ( r = 0.40). Maximum attainable biomass yield (0.87 kg m –2 ) coincided with the period of maximum nutrient uptake, indicating that harvesting cattail between late August and early September maximizes nutrient removal. In situ burial of PRS probes appears to be an effective method of measuring availability of macronutrients but may have limited effectiveness for Cu and Zn.
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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".