Estimating PAH sources in harbor sediments using diagnostic ratios
Bibliographic record
Abstract
Abstract Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the global environment and are subsequently transported into aquatic sediments. As PAHs are formed by various processes, source identification using diagnostic ratios can provide insight to PAH emission sources to distinguish between pyrogenic and petrogenic PAH sources. PAH diagnostic ratios were applied as a forensic source apportionment technique to assess aggregate historical sediment data from 31 small craft harbors (SCHs) across Nova Scotia, Canada. Multiple diagnostic ratios suggest that PAHs present in Nova Scotia SCH sediments are pyrogenic (combustion) in origin, while consistently suggesting that coal‐related PAH sources are potential dominant specific sources. National Institute of Standards and Technology Standard Reference Materials (SRMs) were used as reference for coal tar, urban dust, and diesel exhaust particulates in ratio applications. The SRM for coal tar was most similar to Nova Scotia SCH sediments in multiple ratio applications. Diagnostic ratio results were corroborated by comparing the PAH profile of sediments to source profiles from the literature. Results indicate that Nova Scotia SCH sediments follow global trends by exhibiting a dominant pyrogenic PAH signature, and the specific coal‐related PAH signature of Nova Scotia SCH sediments may be influenced by contamination inputs related to historical industrial coal mining and combustion activities in the province.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| 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".