Plasma to egg conversion factor for evaluating polychlorinated biphenyl and DDT exposures in great horned owls and bald eagles
Bibliographic record
Abstract
The benefits of nondestructive sampling techniques, such as plasma sampling, to directly measure contaminant exposure levels in at-risk or protected raptor populations are many. However, such assays are generally inconsistent with the most certain source of toxicity reference values, which are based on feeding studies and quantified as dietary or "in ovo" (egg-based) concentrations. An accurate conversion factor to translate nondestructive plasma-based contaminant concentrations to comparable egg-based concentrations will prove valuable to risk assessors investigating the potential effects of chemical exposures to raptors. We used databases describing the concentrations of total polychlorinated biphenyls (PCBs) in great horned owls (GHO; Bubo virginianus) and total PCBs and p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) in bald eagles (Haliaeetus leucocephalus) from the Great Lakes region (Michigan, Wisconsin, USA) to develop a relationship to predict concentrations of PCBs and DDE in eggs. To develop a robust predictive relationship, all of the source data included concentrations of both total PCBs and/or DDE for nestling blood plasma and egg samples collected from within discrete active nesting territories and, in most instances, the same nest. The key characteristics (slope and elevation) of each relationship were tested for differences related to species and geographic region. Predicted variability of relationships were examined and compared to variability associated with natural systems. The results of statistical testing indicate that applying the conversion factors between species (GHO to bald eagle) and among geographic regions yields predicted egg concentrations that are not statistically dissimilar and are within the natural variability observed for residue concentrations among eggs of raptors within species and region.
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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 | 0.006 |
| 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".