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Record W7043942593

Understanding Freshwater Ecosystems and Human Health Implications in Recreational Water through Microbial Characterization, Source Tracking, and Sediment-Microbe Dynamics

2022· dissertation· en· W7043942593 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2022
Typedissertation
Languageen
FieldMaterials Science
TopicX-ray Diffraction in Crystallography
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationIndicator organismOrganismWater qualityFreshwater ecosystemEcosystemEcosystem healthContaminationWaterborne diseasesHuman health
DOInot available

Abstract

fetched live from OpenAlex

Contamination of natural aquatic ecosystems is a serious global concern as populations increase and the environment is impacted by climate change. Nonpoint source (NPS) contamination of allochthonous materials, such as sediments, nutrients, and microorganisms, is commonly introduced to a body of water through runoff and wash-off which cumulates over a large area, and is subsequently transported to surface waters (e.g., rivers, streams, lakes) and shorelines. The principal form of microbial contamination of water resources is often from fecal pollution derived from humans, domesticated animals, or wildlife, and contains a variety of human pathogens. There are also numerous factors (with limited research) affecting pathogen survival, persistence, and growth in these environments, complicating research models and progress, and our overall understanding of the microbiology of natural waters. Thus, the potential for human health risk associated with recreational water use can be difficult to recognise and regulate without appropriate testing to identify and characterize the pathogenic profile in these environments. Traditional water quality assessments involve the use of an indicator organism (e.g., E. coli) as a proxy for fecal contamination in recreational waters. However, there are several limitations to these simplistic approaches which lead to unreliable water quality evaluations. These tests 1) are infrequent, time consuming, and nonrepresentative of in situ conditions; 2) target only one organism but omit other waterborne pathogens; 3) involve culture-based techniques or the use of environmental DNA, which cannot inform on microbial activity; 4) neglect to identify contamination origin or source; and perhaps the most significant shortcoming of these assessments is that they 5) overlook the sediment compartment, assuming pathogenic microbes only have planktonic lifestyles. The research presented though this dissertation aims to address the knowledge gap regarding the concern for human health implications involving microbial contamination associated with recreational water use. A spatiotemporal microbial biosignature was first established for freshwater bed sediment in Laurentian Great Lakes beaches. This baseline allowed for focused mRNA-based metatranscriptomic and rRNA-based targeted transcriptomic assessments of both bed and suspended sediment fractions of the nearshore swimming zone. Results indicated significant microbial activity (through diverse metabolic functions as well as pathogenic-related gene expression) associated with both sediment fractions, suggesting freshwater sediment acts as a reservoir and secondary source for microorganisms (including waterborne pathogens) through sediment dynamics (e.g., erosion, resuspension, transport, deposition). Microbial biomass and activity were typically upregulated at low-energy, fine-grained locations, such as Belle River and Kingsville, Ontario beaches. Microbial source tracking (MST) evaluations determined avian sources (i.e., gulls and geese) to be the largest NPS of fecal indicator bacteria (FIB) associated with the sediment compartment along these freshwater shorelines. MST targets provided superior results over general FIB targets and traditional water quality assessments by exposing contamination source details. The results obtained from this research significantly improve our understanding of freshwater ecosystems and human health implications in recreational water through microbial characterization (i.e., expansive community profiling and gene expression studies), MST, and sediment-microbe relationships.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.262
Teacher spread0.223 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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