Identification of Potential Novel Bioaccumulative and Persistent Chemicals in Sediments from Ontario (Canada) Using Scripting Approaches with GC×GC-TOF MS Analysis
Bibliographic record
Abstract
This work describes a single and fast approach using a filtering script as a means of prioritizing sample processing of data acquired by GC×GC-TOF MS for the identification of potentially novel persistent and bioaccumulative halogenated chemicals. The proposed script is based on the recognition of a generic halogenated isotope cluster pattern that allows for the simultaneous detection of chlorinated, brominated, or mixed halogen-substituted compounds in a single classification. Once developed, the script was applied to the identification of organohalogens in stream sediments collected across the southern region of Ontario (Canada). Classified peaks were first compared with available analytical standards and reference libraries to confirm the known chemicals. Unknown potential persistent organic pollutants (POPs) were evaluated for occurrence within the samples and high resolution mass spectrometry was used in order to identify some of the most prevalent compounds in the samples and resulting in the identification of three decachlorinated dechlorane analogs (C18H14Cl10), two undecachlorinated dechlorane species (C18H13Cl11), and a novel mixed chloro/bromo-carbazole (C12H5NCl2Br2) in a number of sediments analyzed. Relative peak abundances of these unknown halogenated compounds were in the same order of magnitude or slightly higher than levels observed for conventional POPs detected in the samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".