Non-target assessment of the maternal transfer of non-polar toxic organic compounds in European eels by GCxGC-TOF and GC-FTICR-MS
Bibliographic record
Abstract
Chemical pollution is hypothesized as one of the factors driving the strong decline of the critically endangered European eel population. Specifically the impact of contaminants on the quality of spawning eels and subsequent embryo survival and development has been discussed as crucial investigation point. However, so far only very limited information on potential negative effects of contaminants on the reproduction of eels is available. Through the combination of non-targeted ultra-high resolution mass spectrometry and multidimensional gas chromatography, combined with more conventional targeted analytical approaches, compounds of particular relevance and their maternal transfer in artificially matured European eels from the German river Ems have been identified. Substituted diphenylamines were, unexpectedly, found to be the primary organic contaminants in the eel samples, with concentrations exceeding 10 µg g-1 ww. Furthermore, it could be shown that these contaminants, as well as known contaminants in eel such as polychlorinated biphenyls (PCBs), organochlorine pesticides and polyaromatic hydrocarbons (PAHs), are not merely stored in lipid rich tissue of eels, but maternally transferred into gonads and eggs. The results of this study provide unpreceded information on both the fate and behavior of substituted diphenylamines in the environment as well as their relevance as contaminants in European eels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".