Critical load analysis in hazard assessment of metals using a Unit World Model
Bibliographic record
Abstract
A Unit World approach has been used extensively to rank chemicals for their hazards and to understand differences in chemical behavior. Whereas the fate and effects of an organic chemical in a Unit World Model (UWM) analysis vary systematically according to one variable (fraction of organic carbon), and the chemicals have a singular ranking regardless of environmental characteristics, metals can change their hazard ranking according to freshwater chemistry, notably pH and dissolved organic carbon (DOC). Consequently, developing a UWM approach for metals requires selecting a series of representative freshwater chemistries, based on an understanding of the sensitivity of model results to this chemistry. Here we analyze results from a UWM for metals with the goal of informing the selection of appropriate freshwater chemistries for a UWM. The UWM loosely couples the biotic ligand model (BLM) to a geochemical speciation model (Windermere Humic Adsorption Model [WHAM]) and then to the multi-species fate transport-speciation (Transpec) model. The UWM is applied to estimate the critical load (CL) of cationic metals Cd, Cu, Ni, Pb, and Zn, using three lake chemistries that vary in trophic status, pH, and other parameters. The model results indicated a difference of four orders of magnitude in particle-to-total dissolved partitioning (K(d)) that translated into minimal differences in fate because of the short water residence time used. However, a maximum 300-fold difference was calculated in Cu toxicity among the three chemistries and three aquatic organisms. Critical loads were lowest (greatest hazard) in the oligotrophic water chemistry and highest (least hazard) in the eutrophic water chemistry, despite the highest fraction of free metal ion as a function of total metal occurring in the mesotrophic system, where toxicity was ameliorated by competing cations. Water hardness, DOC, and pH had the greatest influence on CL, because of the influence of these factors on aquatic toxicity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".