Chlorinated pesticide concentrations, with an emphasis on polychlorinated camphenes (toxaphenes), in relation to cytochrome P450 enzyme activities in harp seals (<i>Phoca groenlandica</i>) from the Barents Sea
Bibliographic record
Abstract
Abstract Harp seals (Phoca groenlandica) from the Barents Sea were analyzed for blubber levels of polychlorinated biphenyls (PCBs), polychlorinated camphenes (PCCs; toxaphenes), DDT and its metabolites, hexachlorobenzene (HCB), hexachlorocyclo-hexanes (HCHs), and the cyclodiene pesticides, including dieldrin, endrin, and the chlordanes. Also, the hepatic cytochrome P450 (CYP) enzyme activities were measured to assess a possible relation between CYP activities and pesticide levels. Furthermore, the bioaccumulation potential and persistency of these compounds were evaluated. PCCs were the dominant contaminants, exceeding the PCB concentrations. Individual PCC congener levels (Tox 26 and 50) were fourfold greater than those in the Canadian Arctic and 20-fold greater than those in seals from the west coast of Svalbard, suggesting that the Barents Sea is exposed to PCCs by a local source. The biomagnification factor and the metabolic index were greatest for p,p-DDT, HCB, β-HCH, and the chlordanes trans-nonachlor and U82. The other pesticides showed lesser values, suggesting metabolism. The ethoxyresorufin-O-deethylation activity (CYP1A) was high and not correlated with any of the pesticides, whereas a high correlation (r2 adjusted > 50%) was found between the PCCs and testosterone 6-β hydroxylation activities (CYP3A). This suggests an induction of CYP3A-like activity by PCC exposure.
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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 | 0.000 |
| 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) | 0.000 | 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".