Contribution of Coal Tar Sources to Polycyclic Aromatic Compounds and Associated Ecological Risk in Lake Ontario Sediments: Inference from a Novel Marker
Bibliographic record
Abstract
Coal tar-related products as a source of polycyclic aromatic compounds (PACs) are particularly concerning due to high PAC concentrations and inadequate source management. Benzo[b]carbazole, a benzocarbazole isomer exclusively found in coal tar-derived products, acts as an ideal marker to distinguish coal tar sources from others, enabling more robust quantification of coal tar contributions to PACs. To evaluate the historical and recent contributions of coal tar-related sources to the levels of PACs in Lake Ontario and associated ecological risk, we analyzed 31 PACs and 3 BCBz isomers in surface sediments and a sediment core. In the surface sediments, summed concentrations of the PACs ranged from 170 to 11,000 ng/g, dry weight, 63-90% of which were attributed to the 16 EPA polycyclic aromatic hydrocarbons (PAHs). Our results suggest the contribution by coal tar-related sources to PAC contamination has increased over the past decades and reached over 40% in Lake Ontario surface sediments. Employing the toxicological-priority-index scheme for the field data, high molecular weight EPA PAHs were identified as priority PACs. Cumulative PAC risk assessments indicate that with the exception of Mimico Creek, all other sampling sites exhibited elevated risk values.
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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 | 0.001 | 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) | 0.000 | 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".