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Record W2548793585 · doi:10.3390/w8110503

Microbial Community Assessment in Wetlands for Water Pollution Control: Past, Present, and Future Outlook

2016· article· en· W2548793585 on OpenAlexaff
Kela P. Weber

Bibliographic record

VenueWater · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsEnvironmental scienceMicrobial population biologyWetlandScale (ratio)GeographyEcologyBiologyCartography

Abstract

fetched live from OpenAlex

The field of treatment wetlands (TWs) is rapidly expanding and, arguably, is tasked with studying and understanding one of the most complex water treatment systems available. Microbial communities are generally considered to be responsible for the majority of wastewater constituent degradation in TWs. However, they are also known to be spatially heterogeneous, temporally dynamic, as well as structurally and functionally diverse. Presented here is a meta-analysis of all peer reviewed TW journal articles which utilized a microbial community assessment methodology over the period of 1988 to July 2016. A total of 1101 papers were reviewed, 512 from 1988 to 2012, 215 of which included a microbial community assessment aspect and were subsequently classified as representing past research, and 589 from 2013 to July 2016, 196 of which were classified as representing current TW microbial community research. In general, TW microbial community research has increased over time, with a marked surge in the past four years. Microbial community structure is currently the most commonly used methodological type followed by activity, enumeration and function, respectively. Areas of research focus included nitrogen transformations (156), organic degradation (33), and emerging contaminants (32), with general characterization studies also accounting for a significant proportion (243). Microbial communities from a range of TW systems have been investigated over the last four years with meso-scale (10–1000 L) being the most commonly studied system size followed by large-scale (>100,000 L), micro-scale (<10 L), and pilot-scale (1000–100,000 L). Free water surface flow (SF), horizontal subsurface flow (HF), and vertical flow (VF) systems are being studied in approximately equal proportions with the majority of studies focused on gaining fixed media/biofilm samples for analysis (rather than from the rhizosphere or interstitial water). Looking at efforts from a regional perspective shows Asia to be publishing the majority of research with a main focus on VF systems and structural community assessment. European and North American studies are generally more evenly distributed among structure, function, activity, and enumeration with the majority of studies completed on HF systems. South America, Africa, and Oceania published fewer studies but focused on structural community assessment with a selection of HF, SF and VF investigations. Great strides are being made in the field of microbial community assessment in TWs with functional assessment methods being developed, better utilized, and being related directly to water treatment. The use of high-powered metagenomics sequencing such as Illumina HiSeq instrumentation is on the rise, as is the development and utilization of functional assays such as DNA microarrays and community level physiological profiling allowing for more complete community assessment. Used in concert with activity, enumeration and newly implemented stable isotope methodologies, the field of TWs is certainly moving away from the black-box understanding of the past.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.585

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 communication0.0000.000
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations38
Published2016
Admission routes1
Has abstractyes

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