Field application of a model of proton and metal mixture bioavailability and effects
Bibliographic record
Abstract
Quantitatively predicting of the responses of freshwater ecosystems to the effects of potentially toxic metals and acidification is an ongoing challenge in ecotoxicology. The WHAM-FTOX model is based on the assumptions that toxic effects of protons and metal cations are additively related to their occupancies of binding sites on organisms, and that those binding sites can be represented by the binding sites of humic acid (HA). We applied the model to simulate the species richness (nsp) of crustacean zooplankton in acid- and metal-contaminated lakes near Sudbury, Ontario between 1973 and 2006. Historic emissions from metal smelters at Sudbury have caused contamination of surrounding lakes by acid deposition, while lakes closest to the smelters were \nalso contaminated with metals, mainly Ni and Cu, and to lesser extents Zn, Cd, and Pb. Changes in water chemistry over the study period show partial recovery as a result of emission reductions. In application, binding of protons and metals to organisms is simulated by applying the WHAM7 model for each water sample. A combined dose term, FTOX,i, is computed for each species within a conceptual assemblage, assuming a distribution of species sensitivities to metals. The probability of finding the species in a sample is then computed from a fixed relationship with FTOX,i, and nsp is computed by summing these probabilities across all species. The distribution of species sensitivities is found by assuming them to be lognormally distributed and fitting their mean and standard deviation. The model was able to describe the variability in nsp well, with an R-squared value of 0.84 (p < 0.0001). Generally, the model \nreproduces the temporal patterns of nsp in individual lakes well, although it tends to underestimate the rate at which species richness increases as water chemistry recovers. The most important toxic cations were H, Al, Ni, and Cu, with a small contribution from Zn. The predicted contributions of protons and individual metals to toxicity generally showed declines over time in the contributions of protons and Al as recovery from contamination took place. However, in some cases the contribution of Ni increased over time, despite reductions on the dissolved concentration. This illustrates the potentially complex interplay among water chemistry variables determining metal exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".