Development and validation of a biotic ligand model for predicting chronic toxicity of lead to <i>Ceriodaphnia dubia</i>
Bibliographic record
Abstract
While it is increasingly being recognized that biotic ligand models (BLMs) are valuable in the risk assessment of metals in aquatic systems, the development of chronic BLMs has been less advanced for lead than for other metals. The authors investigated the univariate effects of Ca and pH on the chronic reproductive toxicity of Pb to Ceriodaphnia dubia at 4 levels. Calcium influenced chronic Pb toxicity to C. dubia only to a relatively small extent, whereas a high pH (8.2) provided strong protection against Pb toxicity (compared with lower pH levels). Based on this data set, a chronic Pb BLM for C. dubia was developed. The effect of pH was modeled as a single biotic ligand site competition by H(+) with a log stability constant for binding of H(+) to the biotic ligand (K(HBL)) of 7.6, while no other competitive constants were needed. The developed BLM was shown, in an independent validation with 3 other data sets, to be capable of predicting chronic Pb toxicity to different clones of C. dubia by an error of less than a factor of 2 in most synthetic and natural waters considered. The results add to the growing evidence that BLM-based risk assessment or water-quality criteria for Pb are likely to be more appropriate relative to hardness-based assessments or criteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".