Application of Biotic Ligand and Toxic Unit Modeling Approaches to Predict Improvements in Zooplankton Species Richness in Smelter-Damaged Lakes near Sudbury, Ontario
Bibliographic record
Abstract
Using a 30-year record of biological and water chemistry data collected from seven lakes near smelters in Sudbury (Ontario, Canada) we examined the link between reductions of Cu, Ni, and Zn concentrations and zooplankton species richness. The toxicity of the metal mixtures was assessed using an additive Toxic Unit (TU) approach. Four TU models were developed based on total metal concentrations (TM-TU); free ion concentrations (FI-TU); acute LC50s calculated from the Biotic Ligand Model (BLM-TU); and chronic LC50s (acute LC50s adjusted by metal-specific acute-to-chronic ratios, cBLM-TU). All models significantly correlated reductions in metal concentrations to increased zooplankton species richness over time (p < 0.01) with a rank based on r(2) values of cBLM-TU > BLM-TU = FI-TU > TM-TU. Lake-wise comparisons within each model showed that the BLM-TU and cBLM-TU models provided the best description of recovery across all seven lakes. These two models were used to calculate thresholds for chemical and biological recovery using data from reference lakes in the same region. A threshold value of TU = 1 derived from the cBLM-TU provided the most accurate description of recovery. Overall, BLM-based TU models that integrate site-specific water chemistry-derived estimates of toxicity offer a useful predictor of biological recovery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".