Risk assessment methodologies for exposure of great horned owls (<i>Bubo virginianus</i>) to PCBs on the Kalamazoo river, Michigan
Bibliographic record
Abstract
Dietary exposures of great horned owls (GHO; Bubo virginianus) to polychlorinated biphenyls (PCBs) in the terrestrial food web at the Kalamazoo River, Michigan, USA, were examined. Average potential daily doses (APDD) in GHO diets were 7- to 10-fold and 3-fold greater at the more contaminated location versus a reference location for site-specific exposures quantified as total PCBs and 2,3,7,8-tetrachlorodibenzo-p-dioxin equivalents (TEQ(WHO-Avian)), respectively. Wetland/aquatic prey contributed significantly to PCB exposure and APDD. Estimates of risk based on comparison of modeled dietary intake (e.g., APDD) to toxicity reference values (TRVs), using a hazard quotient (HQ) methodology, varied between diet composition methods (mass basis vs numeric basis). Mass-basis compositions yielded greater HQs at all sites. Potential risks associated with dietary exposures ("bottom-up" risk assessment methodology) were less than (HQ < 1) benchmarks for effects. This result is consistent with risk estimates based on concentrations in tissues ("top-down" risk assessment methodology), and indicated PCBs posed no significant risk to terrestrial raptor species. Colocated and concurrent studies that evaluated GHO reproductive performance (nestling productivity) and relative abundance were consistent with results of the risk assessment. Measures of risk based on HQs were consistent with direct measures of ecologically relevant endpoints (reproductive fitness). Uncertainty in risk estimates is contributed during the selection of TRVs for effects in GHO based on TEQ(WHO-Avian) because of the absence of species-specific, dose-response thresholds. This evaluation indicated that a multiple-lines-of-evidence approach provided the best estimate of risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".