Congener-Based Aroclor Quantification and Speciation Techniques: A Comparison of the Strengths, Weaknesses, and Proper Use of Two Alternative Approaches
Bibliographic record
Abstract
This paper compares two previously published methods, an Aroclor estimation method and a mixing model method, that relate Aroclor contamination to congener specific data in environmental samples. The Aroclor estimation method, which is consistent with U.S. EPA Method 8082, uses a limited set of congener specific data to estimate Aroclor contributions to the sample, while the mixing model method uses the full congener data to model sample compositions as linear combinations of Aroclors. The performance of these methods are compared, using 181 samples at a variety of trophic levels, in terms of (a) total PCB concentrations, (b) compositional modification levels from original Aroclors, and (c) determination of the Aroclor mixture or mixtures best describing the sample (Aroclor speciation). We find that the two methods agree in all three terms for samples of low trophic level, but disagree for samples of higher tropic levels. Most significantly, the comparison reveals systematic overestimation of total PCB content by the Aroclor estimation method for samples at high trophic levels. The implication is that Aroclor determinations using persistent congeners cannot reliably be used as surrogates for total PCB concentration. The strengths and weaknesses of each method are detailed.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".