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Record W2775400594 · doi:10.2166/wst.2017.624

Plant growth and nutrient uptake in treatment wetlands for water with low pollutant concentration

2017· article· en· W2775400594 on OpenAlexaff
Gilles Vincent, Kankan Shang, Guowei Zhang, Florent Chazarenc, Jacques Brisson

Bibliographic record

VenueWater Science & Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhragmitesMacrophyteEnvironmental scienceNutrientBiomass (ecology)PollutantTypha angustifoliaTyphaWetlandConstructed wetlandEffluentAquatic plantWastewaterAgronomyEnvironmental engineeringEcologyBiology

Abstract

fetched live from OpenAlex

Abstract The objective of this study was to determine how macrophytes commonly used in treatment wetlands (TWs) respond to water with low pollutant concentration. We measured pollutant removal efficiency and compared growth and nutrient uptake of five macrophytes in demonstration scale units (volume >40 m3) irrigated by water with pollutant concentrations representative of average urban stormwater quality. All species showed a strong productivity gradient along the beds, starting with high biomass – high density near the inlet, then decreasing progressively with distance. Cyperus was by far the most productive species. Phragmites and Thalia had higher biomass in the first few metres of the beds than Typha and Arundo. In terms of pollutant removal, decreasing plant growth may be interpreted as indicative of high efficiency when caused by nutrient depletion. Differences in aboveground biomass between species did not translate into measurable differences in removal efficiency at the outlet. Although Phragmites australis is the species most commonly used in TWs, under the low nutrient load, Cyperus had twice its biomass, and higher N and P uptake. These results highlight the importance of considering wastewater characteristics when selecting macrophyte species for TWs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.716

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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