Validated methods for the identification of new halogenated polycyclic aromatic compounds in the Canadian environment
Bibliographic record
Abstract
Polycyclic aromatic compounds (PACs) represent a complex and structurally diverse group of aromatic organic contaminants of both petrogenic and pyrogenic origins. These compounds are released into the environment from anthropogenic and natural sources as complex mixtures, encompassing thousands of different aromatic, alkyl-aromatic, and heterocyclic hydrocarbons containing N-, S-, or O- atoms. Overwhelmingly, most of the research in this field has focused on the sixteen 16 US Environmental Protection Agency priority polycyclic aromatic hydrocarbons (PAHs). However, there is still a large knowledge gap on the identification and quantitation of other PACs, for instance halogenated PAHs (HPAHs), which has led to the overarching hypothesis of my thesis: additional PACs are present in the environment and gas chromatography coupled to mass spectrometry can be used to accurately identify and quantify them. The specific class of PACs that I was interested in studying were ones containing halogen atoms. The rationale for this is that the addition of a halogen on the PAC backbone will increase their environmental persistent and bioaccumulative potential relative to non-halogenated species. The first advancement I made was the detection of HPAHs in environmental samples from the Athabasca Oil Sands Region (AOSR). To my knowledge, this is the first report on the detection of HPAHs from AOSR. The second major advancement I made was the validation of two extraction methods for PACs for abiotic (sediment) and biotic (avian egg) samples, respectively. In both studies, I was able to significantly streamline the sample preparation process of PACs. I also compared various quantification methods for PAC analysis which has led to improved measurement accuracy and precision. The overall results of my work will impact future studies on the development of even faster and more cost-effective analytical monitoring techniques, tools designed to delineate sources of crude oil exposures, and forensic studies leading to new areas of analytical research.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".