Complexity in multimedia mass balance models: When are simple models adequate and when are more complex models necessary?
Bibliographic record
Abstract
Three environmental multimedia models of varying degrees of complexity are compared to assess when simple models are adequate and when more complex models are advantageous. The simplest model, the level II (L-II) model, assumes all environmental media are at chemical equilibrium, whereas the more complex models treat chemical disequilibrium between well-mixed media (standard level IV [L-IV] model) or the major media are subdivided into separate layers to simulate heterogeneity (high-resolution level IV [HR-IV] model). The three models are compared for their performance in predicting steady-state, regional concentrations; dynamic, local-scale concentrations; and chemical persistence in the environment. The results indicate that the L-IV model often provides adequate regional simulations when chemical emission occurs to air or water. This model also is useful for assessing chemical persistence in both steady-state and dynamic scenarios. More complex models, such as the HR-IV model, are suggested for local-scale, dynamic simulations or when the chemical emission occurs to soil because they better characterize rates of intramedia transport, which can greatly affect the model predictions. The simplest L-II model predicts environmental concentrations that can differ significantly from those of more complex models, but it is useful for establishing partitioning tendencies and for ranking chemicals for their relative persistence in steady-state situations.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".