Development of a multiple linear regression model for chronic nickel toxicity to <i>Ceriodaphnia dubia:</i> performance of bicarbonate vs. pH as toxicity modifying factors
Bibliographic record
Abstract
Multiple linear regression models were developed to predict chronic nickel (Ni) toxicity to Ceriodaphnia dubia under an expanded range of conditions relative to published datasets. Test conditions were expanded to study Ni toxicity under very high hardness (up to 1,020 mg/L as CaCO3) and bicarbonate (up to 366 mg/L HCO3) by conducting tests in synthetic waters and mine-influenced site waters. Toxicity modifying factors (TMFs) identified by the models were hardness, dissolved organic carbon, and either pH or bicarbonate. Because high pH and high bicarbonate co-occur in some mine-influenced waters and their relative importance as TMFs for Ni is unclear, we compared the performance of models with each candidate TMF. The model with bicarbonate performed better than the model with pH and showed closer alignment between site-specific and published datasets, supporting bicarbonate as a TMF in both datasets. The model with bicarbonate performed well across the expanded range of conditions and is expected to be more robust than previous Ni models under the high hardness and bicarbonate conditions studied. Comparison of TMF effects on Ni toxicity to other invertebrates indicated stronger support for a bicarbonate TMF effect than pH for some species, including C. dubia. Further support came from site-specific testing that indicated bicarbonate is not toxic to C. dubia at the concentrations in our dataset. These findings suggest bicarbonate may play an important role in modifying chronic Ni toxicity to C. dubia in alkaline waters. More work is needed to understand the mechanism for bicarbonate and pH TMF effects and why TMF effects are species-specific to reduce uncertainties associated with collinearity in model datasets and in applying models across species.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".