New organochlorine contaminants and metabolites in plasma and eggs of glaucous gulls (<i>Larus hyperboreus</i>) from the Norwegian Arctic
Bibliographic record
Abstract
The present study investigated new or lesser-studied and legacy organochlorine (OC) contaminants and metabolites in plasma and eggs of glaucous gulls (Larus hyperboreus) collected from major breeding colonies on Bear Island in the Norwegian Arctic. Hexachlorobutadiene was below the method limit of detection (< 0.07 ng/g lipid wt) in all samples. The sum (sum) of 20 chlorobornane congener concentrations ranged between 294 and 986 ng/g lipid weight and 104 and 1,121 ng/g lipid weight in plasma and eggs, respectively, whereas those of sum 20polychlorinated naphthalene ranged between 1.34 and 126 ng/g lipid weight in plasma and 1.82 and 162 ng/g lipid weight in eggs. Bis(4-chlorophenyl) sulfone concentrations ranged between 5.24 and 143 ng/ g lipid weight plasma, which is the first report of this contaminant in arctic biota north of Sweden. Based on 2,3,7,8-tetrachlorodibenzo-p-dioxin toxic equivalents (TEQs), partial dioxin-like toxicity varied between 3.04 and 20.8 ng TEQ/g lipid weight in plasma and 0.94 and 46.5 ng TEQ/g lipid weight in eggs, and largely was due to concentrations of non-ortho polychlorinated biphenyls (PCBs), with a very minor contribution from mono-ortho PCBs and polychlorinated naphthalenes. The major aryl sulfone metabolite in plasma was an unidentified hexachlorinated MeSO2-PCB congener (range: 13.5-551 ng/g lipid wt), whereas the pentachlorinated congeners 3'- and 4'-MeSO2-CB101 (range: 4.49-38.1 ng/g lipid wt) dominated in eggs. The predominant halogenated phenolic compound (HPC) in plasma was consistently the PCB metabolite 4-OH-CB187 (range: 0.29-17.5 ng/g wet wt), whereas in eggs, detectable HPCs were at very low and transient concentrations. As part of a complex profile of contaminant exposure, these chemical classes and metabolites may be contributing factors to enhance physiological stress in breeding glaucous gulls.
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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 | 0.000 | 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) | 0.000 | 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".