Identifying and Quantifying Environmental Contaminants in Various Matrices using Mass Spectrometry
Bibliographic record
Abstract
Human impact on the environment can be seen in the wide variety of chemicals that are found in our water and soil. Common contaminants arise from insufficient treatment in wastewater treatment plants (WWTPs), and runoff from agriculture. Surface water samples collected in 2017-2020 at 40 different sites in six different watersheds were analyzed to determine if the commonly targeted emerging substances of concern (ESOCs) are present in Ontario and Quebec waterways. The diabetes medication metformin was analyzed more closely alongside its degradation product guanylurea as they have become targets of increasing interest due to their common occurrences and new toxicological data on organisms. Sediment samples at the same sampling sites were also collected. This work is to our knowledge the first long term analysis of metformin and guanylurea in Ontario and Quebec and offers potentially valuable insight into where metformin and guanylurea partition and accumulate in waterways.\nBiosolid samples from Ontario WWTPs were also analyzed for the presence of ESOCs to determine if treatment used to remove micropollutants and bacteria is also able to remove common chemical contaminants. Two extraction methods were assessed to determine their efficacy in extracting a wide variety of compounds. The concentrations of extracted compounds were also compared between the untreated biosolid cake and treated fertilizer to determine if a thermal hydrolysis process (THP) could degrade ESOCs. This work serves to validate a widespread biosolid analysis method for use in further ecotoxicological studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".