A food web bioaccumulation model for organic chemicals in aquatic ecosystems
Bibliographic record
Abstract
The present study examines a new bioaccumulation model for hydrophobic organic chemicals in aquatic food webs. The purpose of the model is to provide site-specific estimates of chemical concentrations and associated bioconcentration factors, bioaccumulation factors, and biota-sediment accumulation factors in organisms of aquatic food webs using a limited number of chemical, organism, and site-specific data inputs. The model is a modification of a previous model and incorporates new insights regarding the mechanism of bioaccumulation derived from laboratory experiments and field studies as well as improvements in model parameterization. The new elements of the model include: A model for the partitioning of chemicals into organisms; kinetic models for predicting chemical concentrations in algae, phytoplankton, and zooplankton; new allometric relationships for predicting gill ventilation rates in a wide range of aquatic species; and a mechanistic model for predicting gastrointestinal magnification of organic chemicals in a range of species. Model performance is evaluated using empirical data from three different freshwater ecosystems involving 1,019 observations for 35 species and 64 chemicals. The effects of each modification on the model's performance are illustrated. The new model is able to provide better estimates of bioaccumulation factors in comparison to the previous food web bioaccumulation model while the model input requirements remain largely unchanged.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".