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Record W6987298849

Source tracking of escherichia coli in a freshwater lake in northwestern ontario (Boulevard Lake, Thunder Bay)

2013· dissertation· en· W6987298849 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2013
Typedissertation
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsnot available
Fundersnot available
KeywordsEscherichia coliFecal coliformPopulationBacteroidesWater qualityFecesIndicator bacteriaBay
DOInot available

Abstract

fetched live from OpenAlex

Escherichia coli is regularly used as a fecal indicator bacteria (FIB) in recreational waters but its persistence in the environment brings its use into doubt. A secondary FIB known as Bacteroides has been under a lot of research lately. Because Bacteroides can be measured with quantitative PCR (qPCR) techniques easily, this makes it an effective FIB to detect fecal contamination. Boulevard Lake in Thunder Bay, Ontario experiences instances of elevated levels of E. coli throughout the summer seasons. For the 2011 season the Bacteroides 16S rDNA markers were monitored and compared with the E. coli population. As both of these FIB are found in feces, influxes of fecal contamination would see increases in both populations. The planktonic E. coli population densities exceeded the Canadian Recreational Water Quality Guidelines of 2.30 log CFU 100ml-1 two times throughout the summer season. These were measured at 2.86 and 2.38 log CFU 100 ml-1 on July 21, 2011 and September 2, 2011, respectively. The Bacteroides biomarkers did have any significant increases during these peak periods of E. coli with p > 0.05. This would suggest that the increased levels of E. coli may not have been due to fecal contaminants. Further investigations with a microbial source tracking approach will provide insights to the potential source(s) of E. coli in Boulevard Lake. For both 2010 and 2011, the planktonic E. coli population at Boulevard Lake was monitored.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.234
Teacher spread0.207 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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