Identification of Potential Novel Bioaccumulative\nand Persistent Chemicals in Sediments from Ontario (Canada) Using\nScripting Approaches with GC×GC-TOF MS Analysis
Bibliographic record
Abstract
This work describes a single and\nfast approach using a filtering\nscript as a means of prioritizing sample processing of data acquired\nby GC×GC-TOF MS for the identification of potentially novel persistent\nand bioaccumulative halogenated chemicals. The proposed script is\nbased on the recognition of a generic halogenated isotope cluster\npattern that allows for the simultaneous detection of chlorinated,\nbrominated, or mixed halogen-substituted compounds in a single classification.\nOnce developed, the script was applied to the identification of organohalogens\nin stream sediments collected across the southern region of Ontario\n(Canada). Classified peaks were first compared with available analytical\nstandards and reference libraries to confirm the known chemicals.\nUnknown potential persistent organic pollutants (POPs) were evaluated\nfor occurrence within the samples and high resolution mass spectrometry\nwas used in order to identify some of the most prevalent compounds\nin the samples and resulting in the identification of three decachlorinated\ndechlorane analogs (C<sub>18</sub>H<sub>14</sub>Cl<sub>10</sub>),\ntwo undecachlorinated dechlorane species (C<sub>18</sub>H<sub>13</sub>Cl<sub>11</sub>), and a novel mixed chloro/bromo-carbazole (C<sub>12</sub>H<sub>5</sub>NCl<sub>2</sub>Br<sub>2</sub>) in a number of\nsediments analyzed. Relative peak abundances of these unknown halogenated\ncompounds were in the same order of magnitude or slightly higher than\nlevels observed for conventional POPs detected in the samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.083 | 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 teacher head, 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".