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Record W2900363766 · doi:10.2134/jeq2018.05.0184

Nitrogen and Phosphorus Phytoextraction by Cattail ( <i>Typha</i> spp.) during Wetland‐based Phytoremediation of an End‐of‐Life Municipal Lagoon

2018· article· en· W2900363766 on OpenAlexafffund
Nicholson N. Jeke, Francis Zvomuya, Nazim Çiçek, Lisette Ross, Pascal Badiou

Bibliographic record

VenueJournal of Environmental Quality · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsDucks Unlimited CanadaUniversity of Manitoba
FundersEnvironment and Climate Change Canada
KeywordsPhytoremediationBiosolidsTyphaWetlandEnvironmental sciencePhosphorusNutrientAgronomyGrowing seasonEnvironmental engineeringBiologySoil waterEcologyChemistry

Abstract

fetched live from OpenAlex

Spreading biosolids on farmland can be an effective and beneficial option for managing end-of-life municipal lagoons. Where the spreading of biosolids on farmland is restricted or unavailable, in situ phytoremediation could be a sustainable alternative. This study examined nitrogen (N) and phosphorus (P) phytoextraction by cattail ( spp.) from biosolids in a wetland constructed within a lagoon cell previously used for primary treatment of municipal wastewater. The effect of harvesting season as well as harvest frequency on N and P removal were evaluated. Forty-eight 4-m plots within the constructed wetland were used to determine the effect of cattail harvest frequency on plant N and P phytoextraction. Harvesting twice per season resulted in a 50 to 60% decrease in phytoextraction of N and P relative to a single harvest per season, which produced biomass yields of 0.58 to 0.6 kg m per year and accumulated 36.7 g N m and 5.6 g P m over the 4-yr period. Compared with August, harvesting cattails in November or April reduced N and P phytoextraction by 63 to 85%. These results demonstrate that phytoextraction of nutrients is more effective with a single harvest compared with two harvests per season. Additionally, we found that while harvesting in November and April is appealing logistically (since the wetland is frozen and provides easier access to harvest equipment), nutrient removal rates are significantly reduced.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.240
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2018
Admission routes2
Has abstractyes

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