FAIR and Effective Communication of Data on Chemical Contaminant Biotransformation in the Environment
Bibliographic record
Abstract
Anthropogenic chemicals and their transformation products are increasingly found in the environment, with persistence being a major driver of chemical risk. Methods for predicting biotransformation products and dissipation kinetics are needed to help regulators identify potentially persistent chemicals and prevent their release to the market and eventually to the environment. Leveraging machine learning and artificial intelligence is a promising avenue to tackle this problem. However, predictive models are only as good as the data used to train them, calling for large, high-quality data sets of biotransformation pathways and kinetics, which are currently lacking. The objectives of this Global Perspective are to (i) emphasize the importance of effectively communicating biotransformation data on chemical contaminants in the environment, (ii) describe specific components of reporting biotransformation pathways in a findable, accessible, interoperable, and reusable (FAIR) format, and (iii) provide a standardized tool for researchers to use for reporting their biotransformation data, with the intent to boost the quality and quantity of available biotransformation data. We demonstrate the application of our reporting tool for the case of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as a means to develop a PFAS biotransformation database, thereby illustrating how the research community could profit from standard biotransformation data reporting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".