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Record W2415559596 · doi:10.2175/106143006x99821

Heterotrophic Bacterial Activities and Treatment Performance of Surface Flow Constructed Wetlands Receiving Woodwaste Leachate

2006· article· en· W2415559596 on OpenAlexafffund
Wendong Tao, Ken J. Hall, Sheldon J.B. Duff

Bibliographic record

VenueWater Environment Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaMicrosoft
KeywordsLeachateEnvironmental scienceWetlandHeterotrophSurface waterSubsurface flowEnvironmental engineeringConstructed wetlandEnvironmental chemistrySewage treatmentChemistryEcologyBiologyGroundwaterBacteriaGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Heterotrophic activities were investigated by measuring 3H-leucine incorporation to bacterial protein and 14C-glucose turnover in surface flow constructed wetlands receiving woodwaste leachate. No significant longitudinal variation was found in heterotrophic activities of bacterioplankton. An open wetland, a vegetated wetland, and a fertilized vegetated wetland were used to examine the effects of vegetation and ammonium nitrate amendment. There was not a significant difference in treatment performance among the three wetlands, except for a significant pH increase and more efficient volatile fatty acids removal in the fertilized wetland. The fertilized wetland had the highest leucine incorporation rate and shortest glucose turnover time accompanied by the lowest glucose mineralization percentage, followed by the open wetland, then the vegetated wetland. Planktonic and sedimentary bacteria contributed to the majority of the total heterotrophic activities; epiphytic bacteria played a minor role. Heterotrophic activities were influenced by the availability of nutrient, electron acceptor, and organic substrate.

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 categoriesInsufficient payload (model declined to judge)
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 score0.253
Threshold uncertainty score0.998

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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.

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

Citations10
Published2006
Admission routes2
Has abstractyes

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