Using regional exposure criteria and upstream reference data to characterize spatial and temporal exposures to chemical contaminants
Bibliographic record
Abstract
Abstract Analyses of biomarkers in fish were used to evaluate exposures among locations and across time. Two types of references were used for comparison, an upstream reference sample remote from known point sources and regional exposure criteria derived from a baseline of fish from reference sites throughout Ohio, USA. Liver, bile, and blood were sampled from white suckers (Catostomus commersoni) and common carp (Cyprinus carpio) collected during 1993 and 1996 in the Ottawa River near Lima, Ohio. Levels of exposure were measured for petroleum by naphthalene-type metabolites, combustion by-products by benzo[a]pyrene-type metabolites, coplanar organic compounds by ethoxyresorufin-O-deethylase (EROD) activity, and urea by blood urea nitrogen (BUN) levels. The four biomarkers analyzed proved effective in determining differences between reference and polluted sampling sites, between geographically close (<0.5 km) sites, and between sampling years at sites common in both years. Calculated exposure criteria levels of the polycyclic aromatic hydrocarbon bile metabolites were found to be a conservative approximation of levels from a designated reference site and could thereby permit comparison of biomarker levels of fish from the Ottawa River to a regional reference level. Polycyclic aromatic hydrocarbon bile metabolite and EROD activity levels were more reflective of spatial patterns of contamination than BUN, although all biomarkers indicated differences overtime. Biomarkers from white suckers seemed to be more responsive in detecting changes in contaminant levels than the same biomarkers from common carp. Lower levels in 1996 of all biomarkers at many sites suggested lower exposures than in 1993 and could be indicative of some improvement over the period.
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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 | 0.002 | 0.004 |
| 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) | 0.000 | 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".