Lead toxicity to <i>Lemna minor</i> predicted using a metal speciation chemistry approach
Bibliographic record
Abstract
In the present study, predictive measures for Pb toxicity and Lemna minor were developed from bioassays with 7 surface waters having varied chemistries (0.5-12.5 mg/L dissolved organic carbon, pH of 5.4-8.3, and water hardness of 8-266 mg/L CaCO3 ). As expected based on water quality, 10%, 20%, and 50% inhibitory concentration (IC10, IC20, and IC50, respectively) values expressed as percent net root elongation (%NRE) varied widely (e.g., IC20s ranging from 306 nM to >6920 nM total dissolved Pb), with unbounded values limited by Pb solubility. In considering chemical speciation, %NRE variability was better explained when both Pb hydroxides and the free lead ion were defined as bioavailable (i.e., f{OH} ) and colloidal Fe(III)(OH)3 precipitates were permitted to form and sorb metals (using FeOx as the binding phase). Although cause and effect could not be established because of covariance with alkalinity (p = 0.08), water hardness correlated strongly (r(2) = 0.998, p < 0.0001) with the concentration of total Pb in true solution ([Pb]T_True solution ). Using these correlations as the basis for predictions (i.e., [Pb]T_True solution vs water hardness and %NRE vs f{OH} ), IC20 and IC50 values produced were within a factor of 2.9 times and 2.2 times those measured, respectively. The results provide much needed effect data for L. minor and highlight the importance of chemical speciation in Pb-based risk assessments for aquatic macrophytes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".