Food web bioaccumulation model for polychlorinated biphenyls in San Francisco Bay, California, USA
Bibliographic record
Abstract
We document the development and application of a food web bioaccumulation model for polychlorinated biphenyls (PCBs) in San Francisco Bay, California, USA. The model calculates spatial distributions of PCB concentrations in a range of invertebrate, fish, avian, and mammalian organisms, including harbor seals, double-crested cormorants, and Forster's terns. The performance of the model is evaluated against independent empirical PCB concentrations and shows a mean deviation between observed and model-calculated concentrations of 36% for female harbor seals and 5% for benthic invertebrates and jack smelt. The model was applied to produce bay-wide PCB concentration distributions in fish and wildlife species, which were compared with threshold effect concentrations to determine ecological risks and human health risks of fish consumption. Because of their high trophic position in the food web, harbor seals exhibited the highest concentrations of summation operatorPCBs, which exceeded threshold concentrations for potential adverse effects. The model was also applied to derive bay-wide target sediment concentrations for remediation as part of an ongoing total maximum daily loading characterization. The model calculated bay-wide geometric mean concentrations of summation operatorPCB in sediments of 1.6 to 73 microg/kg dry weight to meet several ecological and human health risk objectives. The bay-wide geometric summation operatorPCB concentration in the sediments at the time of the study was 11.6 microg/kg dry weight. The model was developed for assessing the behavior and risks of bioaccumulative substances on an ecosystem level.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".