EcoToxChip: A next-generation toxicogenomics tool for chemical prioritization and environmental management
Bibliographic record
Abstract
Chemical contamination of natural ecosystems is regarded as one of our planet's greatest threats (Landrigan et al. 2018). Contaminant‐related phenomena such as malformed frogs, fish with tumors, and dwindling bird populations increasingly fuel societal concerns. Legislation in North America and Europe mandates the assessment and reduction of risk for thousands of commercially important chemical substances used by society and released into the environment. For example, large‐scale efforts such as the Chemicals Management Plan in Canada, the European Union's Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) program, and the US Environmental Protection Agency's (USEPA's) ToxCast program under the Toxic Substances Control Act (1976) were implemented to address legislative obligations to identify, prioritize, and take action on chemicals found to be harmful. However, these regulatory programs face significant challenges. The number of chemical substances for which toxicity data are required is tremendous and backlogged (e.g., 23 000 initially in Canada; 85 000 in the United States; upward of 101 000 in the European Union) and continues to grow by approximately 500 to 1000 new substances each year. In addition, regulations such as Canada's Wastewater Systems Effluent Regulations (section 36, Fisheries Act), the US National Pollutant Discharge Elimination System, and the European Union's Water Framework Directive (2000/60/EC) mandate the monitoring of municipal and industrial effluents with regard to their potential impacts on aquatic ecosystem health. These programs require the testing of complex environmental samples (e.g., water, effluents, and sediments) for compliance; however, treatment and remediation efforts represent huge, unresolved challenges for chemicals management stakeholders, including those in regulatory agencies and industry.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".