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Record W2520593801 · doi:10.2136/vzj2012.0092

Sampling <i>Escherichia coli</i> and Total Coliforms using Stainless Steel Suction Lysimeters

2013· article· en· W2520593801 on OpenAlexafffund
Edwin E. Cey

Bibliographic record

VenueVadose Zone Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsLysimeterEffluentIndicator bacteriaEnvironmental scienceWastewaterSampling (signal processing)Fecal coliformBacteriaEnvironmental chemistrySeptic tankEnvironmental engineeringSuctionHydrology (agriculture)ChemistryPulp and paper industrySoil waterSoil scienceWater qualityBiologyEcologyGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Methods for monitoring fecal indicator bacteria in soil pore water are needed to improve characterization of bacterial fate and transport in the vadose zone. Laboratory experiments were conducted using commercially available stainless steel suction lysimeters to assess their capability for sampling total coliform (TC) and Escherichia coli (EC) indicator bacteria. The lysimeters were placed in a liquid‐filled tank containing either wastewater effluent or bacteria‐free solution. During initial sampling, concentrations for both TC and EC were reduced by up to 3‐log (i.e., 1000‐fold) compared to wastewater input concentrations. Bacterial retention rates in the lysimeters decreased to values of approximately 0.5–1.5 log with repeated sampling and during subsequent sampling events. After placing the samplers in essentially bacteria free water, continued detections of TC and EC suggested a memory effect, but concentrations generally returned to within 1.0‐log of the input concentration after two or three samples were collected. The results indicate that stainless steel lysimeters can provide a reliable method for semi‐quantitative enumeration of fecal bacteria indicators in soil pore water.

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 score0.724
Threshold uncertainty score0.527

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)0.0000.000

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 head0.018
GPT teacher head0.230
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

Citations2
Published2013
Admission routes2
Has abstractyes

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