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Record W1971058995 · doi:10.1002/clen.201100308

Plant Species Richness Affected Nitrogen Retention and Ecosystem Productivity in a Full‐Scale Constructed Wetland

2012· article· en· W1971058995 on OpenAlexafffund
Sixi Zhu, Peili Zhang, Hai Wang, Han‐Liang Ge, Jie Chang, Scott X. Chang, Zhi Qiu, Hongbo Shao, Ying Ge

Bibliographic record

VenueCLEAN - Soil Air Water · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsSpecies richnessMonocultureProductivityEcosystemNitrateAmmoniumSubstrate (aquarium)WetlandConstructed wetlandNitrogenAgronomyBiodiversityEnvironmental scienceEnvironmental chemistryEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Abstract The effects of plant species richness (SR; i.e., 1, 2, 4, 8, and 16 species per plot) on substrate nitrate and ammonium retention and ecosystem productivity in a full‐scale constructed wetland (CW) with high nitrogen (N) input were studied. Substrate nitrate (0.1–16.4 mg kg −1 ) and ammonium concentrations (1.3–9.2 mg kg −1 ) in this study were higher than those in other comparable biodiversity experiments. Substrate nitrate concentration significantly increased while ammonium concentration significantly decreased with the increase of plant SR ( p = 0.008 and 0.040, respectively). The response of ecosystem productivity to increasing SR was unimodal with four species per plot achieving the greatest productivity. Transgressive overyielding, which was compared to the most productive of corresponding monocultures, did not occur in most polycultures. We conclude that substrate N retention was enhanced by plant SR even with high N input, and plant SR could be managed to improve the efficiency of N removals in CWs for wastewater treatment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.763

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.181
Teacher spread0.173 · 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 designObservational
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

Citations13
Published2012
Admission routes2
Has abstractyes

Explore more

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