Evaluation of the biotic ligand model to predict long-term toxicity of nickel to <i>Hyalella azteca</i>
Bibliographic record
Abstract
Three models were developed and evaluated for their ability to predict long-term bioaccumulation of nickel (Ni) and its toxicity to Hyalella azteca using data from 28-d toxicity tests. One of the models was based on competitive action of Ni with Ca and H (the biotic ligand model; BLM), and the other two models included expressions for the potential noncompetitive action of calcium on the ligand (i.e., acclimation), in addition to, or instead of, its competitive action (not accounted for in the BLM). Each model was able to predict lethal accumulation 50 (accumulation at 50% mortality; LA50s) within a factor of 2 of the corresponding observed LA50. The mean predicted LA50 from all three models was within 13% of the observed mean LA50 of 0.90 µmol/g (dry weight). The median lethal concentrations (LC50s) predicted by the three models were similar and were within a factor of 2 of the observed LC50s for 11 of 13 tests, providing encouragement for further development of a long-term Ni BLM. The similar performance of models based on competitive or noncompetitive action may reflect limitations in the data set or may suggest that effects of calcium on the ligand (L(T)) were insufficient to hamper the functionality of the competitive model or that the LA50/L(T) ratio, rather than the LA50 and L(T), is constant.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".