Different organochlorine contaminant profiles in groups of flounders (<i>Platichthys flesus</i>) from sampling locations around Denmark
Bibliographic record
Abstract
Flounders ( Platichthys flesus ) from the waters around Denmark were analysed for their organochlorine (OC) profile to study whether fish from the same genetic population could be separated into characteristic subpopulations, based on their feeding grounds. The chemical analysis of fish liver provided a data set of 16 OC compounds in 94 samples from 2004 to 2006. Except for hexachlorocyclohexane, OC compounds were intercorrelated, indicating similar environmental fate and bioaccumulation. OC profiles are less affected than absolute concentrations by potentially confounding biological factors and thus more suitable for studies of intrapopulation differences in relation to feeding grounds. Principal component analysis grouped the samples according to locations. All but three of the 94 samples could be reclassified. Samples from the same and additional locations collected in 2003 provided validation, with only few misclassifications. This statistical separation likely reflects location-specific pollutant patterns in sediments and biota, even on the relatively small scale of this study. Thus, despite the lack of genetic differences, characteristic subpopulations of flounders could be identified with separate feeding grounds. OC profiles have been used increasingly to distinguish stocks.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".