Impacts of Cooking Technique on Polychlorinated Biphenyl and Polychlorinated Dioxins/Furan Concentrations in Fish and Fish Products with Intake Estimates
Bibliographic record
Abstract
Polychlorinated biphenyl (PCB) and polychlorinated dibenzo-p-dioxin (PCDD) and dibenzofuran (PCDF) concentrations were determined in composites of 18 different fish products and were prepared as raw, baked, boiled, and fried. ∑PCB concentrations were found to range from 0.12 ng·g(-1) whole weight (ww) in raw octopus to 33 ng·g(-1) ww in baked mackerel. Boiled monkfish was found to have the lowest ∑PCDD/F concentrations (0.41 pg·g(-1) ww), while maximum concentrations were observed in fried catfish (59 pg·g(-1) ww). PCB and PCDD/F concentrations in fish were generally reduced during cooking, although differences were small. The average PCB reduction in finfish was 7.9%, while an increase in PCB mass was observed in non-finfish (2.9%). PCDD/F losses, on average, were observed in both the finfish (3.6%) and non-finfish products (25%). Maximum ∑PCB, ∑PCDD/F, and TEQ(PCDD/F+DL-PCB) (toxic equivalency) intakes, based on 150 g serving size, were determined to be 3300 ng (mackerel), 6600 pg (catfish), and 270 pg (catfish), respectively. PCB and PCDD/F changes associated with cooking generally were small (<15%), although larger mean differences (∼40%) were observed in some fish products (e.g., catfish).
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".