Similarity of an Aroclor-Based and a Full Congener-Based Method in Determining Total PCBs and a Modeling Approach To Estimate Aroclor Speciation from Congener-Specific PCB Data
Bibliographic record
Abstract
Polychlorinated biphenyls (PCBs) have entered the environment in North America as Aroclor technical mixtures. Most methods used for the determination of total PCB levels in environmental samples visually match patterns of sample peaks to those in Aroclor standards. Concern over the accuracy of Aroclor-based measurements on compositionally modified samples coupled with advancements in analytical techniques have led to congener-specific PCB analysis. In this study, the PCB data from 27 tissue samples determined by an Aroclor-based method and a full congener method were compared in terms of total PCB concentration to assess the reliability of this Aroclor technique for total PCB determination. Our data show a strong correlation between the sum of Aroclors and the total PCBs obtained from the full congener determinations. We also developed a model using the compositional data from three Aroclors (1242, 1254, and 1260) to determine the amount of compositional alteration from original Aroclor patterns in environmental samples. Full congener data, from a variety of tissue types and trophic levels, examined using this method showed that compositional modification from original Aroclor patterns increases with trophic level, with the greatest modification observed in seal and killer whale samples. This result agrees both with expectation and with what has been found in other studies. Such techniques, which connect congener-specific PCB data to Aroclor contamination, may prove useful to investigations into environmental and metabolic fate and transfer processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".