Multiple Linear Regression Modeling Predicts the Effects of Surface Water Chemistry on Acute Vanadium Toxicity to Model Freshwater Organisms
Bibliographic record
Abstract
Abstract Multiple linear regression (MLR) modeling has been successfully used to predict how water chemistry variables influence the toxicity of cationic metals to aquatic organisms, but no MLR model exists for vanadium (V). Recent research has indicated that an increase in pH (from 6 to 9), or high concentrations of sodium (473 mg Na+/L), increase V toxicity to Daphnia pulex. In contrast, increases in alkalinity (>100 mg as CaCO3) and sulfate (>100 mg SO42–/L) reduce V toxicity. How these variables influence V toxicity to Oncorhynchus mykiss (rainbow trout) was still unknown. Our results show that increasing pH from 6.2 to 8.9 tended to decrease the 96-h median lethal concentration (LC50) for V toxicity to O. mykiss by 9.6 mg V/L. An alkalinity increase from 71 to 330 mg/L as CaCO3 tended to increase the 96-h LC50 by 3.3 mg V/L, whereas when SO42− rose from 150 to 250 mg/L, the LC50 significantly increased by 0.3 mg V/L followed by a significant decrease of 1 mg V/L when SO42− was >250 mg/L. Sodium (between 100 and 336 mg/L) showed no effect on V toxicity to O. mykiss. The toxicity patterns for O. mykiss were similar to those observed for D. pulex, except for that of SO42−, potentially indicating different mechanisms of V uptake or regulation in the 2 species. The LC50s and associated water chemistry were combined to develop an MLR model for O. mykiss and D. pulex. Alkalinity and pH modified V toxicity to both species, whereas SO42− influenced V toxicity to D. pulex. Overall, MLR models should be considered for creating new local benchmarks or water quality guidelines for V. Environ Toxicol Chem 2020;39:1737–1745. © 2020 SETAC
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".