Best Practices for Derivation and Application of Thresholds for Metals Using Bioavailability-Based Approaches
Bibliographic record
Abstract
The primary goal of the present study is to provide a broad view of best practices for evaluating bioavailability models for metals for use in the protection of aquatic life. We describe the state of the science regarding 1) the evaluation and selection of ecotoxicity data, 2) the selection of bioavailability models for use in normalization, and 3) subsequent application of bioavailability models. Although many examples of normalization steps exist worldwide, a scheme is proposed to evaluate and select a model that takes account of its representativeness (water chemistry and taxonomic coverage of the ecotoxicity data set) and validation performance. Important considerations for a suitable model are the quantity of inputs needed, accuracy, and ease of use, all of which are needed to set protective values for aquatic life and to use these values to evaluate potential risks to organisms in receiving waters. Although the end results of different model application approaches may be broadly similar, the differences in these application frameworks ultimately come down to a series of trade-offs between who needs to collect the data and use the bioavailability model, the different requirements of spatial scales involved (e.g., regional vs site-specific values), and model predictiveness and protectiveness. Ultimately, understanding the limits and consequences of these trade-offs allows for selection of the most appropriate model and application framework to best provide the intended levels of aquatic life protection. Environ Toxicol Chem 2019;39:118-130. © 2019 SETAC.
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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 | 0.040 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".