Vulnerability of a fractured dolostone aquifer to emerging sewage-derived contaminants and their use as indicators of virus contamination
Bibliographic record
Abstract
The current document had been prepared in the form of three separate papers that are intended to be submitted as individual papers to scientific journals. This thesis is the result of strongly collaborative work involving myself and others at the University of Guelph and researchers at several other institutions where the water analyses were conducted in laboratories with very advanced capabilities. This thesis is written as my contribution to science therefore below I explain my role in the work. The first paper comprising Thesis Chapter 2, focuses on the vulnerability of a fractured bedrock aquifer to contamination with sewage-derived human enteric viruses. For this chapter, my responsibilities included collecting 118 virus samples throughout an 8 month sampling campaign of 22 wells across southern Wellington County, ON and analysing the data provided by Dr. Mark Borchardt’s lab. Dr. Mark Borchardt and his team from the USDA lab in Marshfield Wisconsin provided expertise with regards to virus sampling and analysis methods and strategy. The USDA lab also provided the project with the necessary equipment for the collection of virus samples and completed all virus analyses in their lab paid for by the University of Guelph (Dr. Parker research grants at reduced rates). Drs. Beth Parker and John Cherry provided expert input with regards to the hydrogeological aspects of the project and provided appropriate funding for the virus analyses. After I had compiled and analyzed the data and wrote it up in the form of Thesis Chapter 2, Drs. Mark Borchardt, Beth Parker, and John Cherry all reviewed and commented on this thesis chapter. A specific journal has yet to be determined for publication of this research chapter. Thesis Chapter 3 focuses on the vulnerability of a fractured bedrock aquifer to emerging sewage-derived contaminants including artificial sweeteners, pharmaceuticals and various other anthropogenic wastewater compounds. For this chapter, my responsibilities included collecting water samples from the above mentioned 22 wells and shipping them to various labs where they were analyzed for artificial sweeteners, pharmaceuticals, major ions, various water isotopes, tritium, and other anthropogenic contaminants. Expert insight with regards to the transport, sample collection, and anlaysis of artificial sweeteners was provided by Drs. William Robertson from the University of Waterloo and Dale Van Stempvoort from the Canada Centre for Inland Waters located in Burlington, ON. Dr. Van Stempvoort’s lab provided analysis of groundwater samples for 4 artificial sweeteners and perchlorate at no cost. Dr. Chris Metcalfe and his lab crew at Trent University provided insight into the occurrence of pharmaceuticals in groundwater and conducted all of the analyses of groundwater samples for pharmaceutical compounds at research costs covered by Dr. Parker grants. After I compiled the analysis results and summarized and discussed them in Thesis Chapter 3, Drs. Beth Parker and John Cherry provided insight with regards to the hydrogeologic aspects of the project and provided editorial and scientific feedback on the document presented here. A specific journal has yet to be determined for publication of this research chapter. Thesis Chapter 4 investigates the use of the above mentioned emerging sewage-derived contaminants as novel indicators of virus contamination. As this paper draws from both of the papers comprising Chapters 2 and 3, the collaborators include Drs. Mark Borchardt, Beth Parker, John Cherry, William Robertson, Dale Van Stempvoort, and Chris Metcalfe. Each contributor offered services as mentioned above. Dr. Kari Dunfield is the final collaborator on this final paper as she provided insight with regards to traditional bacterial fecal indicators and the analyses used to assess their presence in groundwater. Dr. Dunfield also provided lab space for these bacterial analyses to occur. Bacterial analyses were conducted by myself and a wide range of field helpers, namely Loic Paquier and Amanda Malenica. After I summarized and discussed the results in Chapter 4, comments on this final paper were provided by Drs. Mark Borchardt, Beth Parker, and John Cherry. A specific journal has yet to be determined for publication of this research chapter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".