Identification of Chemical Feature Profiles in Agricultural and Surface Waters
Bibliographic record
Abstract
DOM (dissolved organic matter) is a complex mixture of natural organic materials made up from multiple sources. The main anthropogenic sources of DOM are agricultural runoff, urban runoff, and/or water treatment activities. DOM sources and character, interactions, and degradation, on spatial and temporal scales, show a complex makeup that is ever changing due to various biotic processes and fluctuating from external and internal sources. Technological advances in mass spectrometry instruments have allowed high mass resolution and mass accuracy analysis of complex mixtures with novel techniques, such as direct infusion of water samples with minimal sample preparation. This thesis investigates the analysis, the identification and potential quantification, of every chemical entity within a specific water sample. Irrigation water in Alberta is important for agriculture, livestock, rural communities, wildlife, and also various recreational activities. However, few studies have monitored these waters over long periods of time and most used different methods and objectives for analysis. Samples between 2016 and 2019, during the month of July, were randomly chosen from the full dataset provided by the Irrigation District Water Quality (IDWQ) website (Alberta’s Irrigation Districts et al, 2023). This dataset included the samples analyzed by mass spectrometry at the Toxicology Centre. Irrigation water samples were successfully analyzed, by direct infusion, with the Thermo Orbitrap instrument. Several hundred peaks were identified as being consistent across samples. The results from both the IDWQ data and the MS data suggested that Secondary and Return locations shared similar characteristics to each other while being somewhat distinct from Primary location samples. Analysis of data presented by the IDWQ, between 2016 and 2019, in July showed that WQI values were consistently lower in irrigation water that was being returned to the natural river source than in the water initially taken from the river. Additionally, median WQI values in Return locations were shown to improve after 2016, highlighting the importance of monitoring water sources and involvement from the local government in improving water quality as various mitigation strategies are applied. The artificial reservoirs Lake Diefenbaker (LD) and Buffalo Pound Lake (BPL) are essential resources for the province of Saskatchewan, Canada and provide water to Saskatchewan residents, agricultural lands, and livestock. A total of 240 individual analyses (including the locations, chosen at random, that were analyzed in triplicate) were conducted on samples, in 2019 and 2021, from Lake Diefenbaker and Buffalo Pound Lake. Features in the mass spectra were identified with the use of an R script. Both BPL and LD PCA analysis of water samples showed that water samples taken in months closest in time tend to cluster together. The months of Summer that overlap the Autumn samples are the latest months of summer. Therefore, there is a consistent and gradual seasonal change of the chemical composition of DOM in the water samples in both lakes. Algorithms written the R statistical software environment were successful in generating chemical formulae for organic compounds and detecting isotopes for identification. Additional adducts and elements are required for more accurate formula generation as well as additional steps to decrease the number of chemical formulae generated for higher masses (>400 m/z).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".