Organic UV absorbents in the deepwater redfish (Sebastes mentella) from the St. Lawrence Estuary and Gulf: Distribution and human health risk assessment
Bibliographic record
Abstract
UV absorbents (UVAs), such as organic UV filters (UVFs) and benzotriazole UV stabilizers (BZT-UVs), are used in a wide range of consumer and industrial products and they are contaminants of emerging concern in the environment. However, their occurrence and fate in the deep-sea environments are inadequately understood. This study investigated the occurrence and distribution of five UVFs and ten BZT-UVs in the muscle (n = 127) of 2019-collected deepwater redfish (Sebastes mentella) from the St. Lawrence Estuary and Gulf (SLEG) (Canada) to better understand the accumulation of these contaminants in deep-sea fish. Small redfish (<30 cm) tended to have higher concentrations of UVAs in the muscle than that of larger specimens (>30 cm). The UVF 2-hydroxy-4-methoxybenzophenone (BP3) was the most frequently detected (present in 34 % of all samples) target UVA, with concentrations as high as 413 ng/g (dry weight). According to the δ15N and δ13C data, pelagic-eating redfish, and individuals with lower trophic levels had higher lipid content and accumulated more BP3 in their muscles. Four BZT-UVs were detected in redfish muscle, but the detection frequency was lower than 30 %. The estimated hazard quotient for these contaminants was <2.3 × 10−2 for general Canadian populations, indicating that they are unlikely to pose health risks to humans through redfish consumption. Factors influencing UVAs bioaccumulation in redfish, as well as the effects UVAs may have on deep-sea species, should be researched further.
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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 | 0.001 |
| 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".