ABAM, a Model for Bioaccumulation of POPs in Birds: Validation for Adult Herring Gulls and Their Eggs in Lake Ontario
Bibliographic record
Abstract
An Avian BioAccumulation Model (ABAM) of persistent organic pollutant (POP) uptake and elimination in adult life-stage of birds was validated by simulation of concentrations of DDE, dieldrin, mirex, and HCB in herring gull eggs in Lake Ontario for the years 1985, 1990, and 1992. These chemicals represented a range of whole-body half-lives of 82-265 days in the gull. Dietary intake of POPs by a female gull was simulated by a dynamic bioenergetics model which included dependence on temperature, photoperiod, egg production, and feeding chicks. Concentrations in the two main prey fish of the gull in Lake Ontario were used for POP exposure. Clearance from the female was based on a two compartment toxicokinetic model. Egg concentrations were estimated from egg/whole body female concentration ratios. Simulated concentrations were compared to measured concentrations in gull eggs from 4 different colonies in the northern part of Lake Ontario. Simulations using a diet of 81% fish and 19% uncontaminated food resulted in the best fit with least variance among predicted and measured data. The mean ratio of predicted to measured concentrations in eggs was 1.0 +/- 0.27 among chemicals, years, and colonies for this exposure scenario. This result was in excellent agreement with field assessments of herring gull diet composition in Lake Ontario of 80-82% fish. The ability to perform accurate a priorisimulations for the range of test conditions employed in the validation constituted a rigorous test of the soundness of the model's structure and parameterization. With species-specific adjustments, ABAM can be regarded as a general model for lipophilic POPs bioaccumulation in birds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".