Create a Peer Review Record documenting the cross-validation findings for the machine-learning predictive model used in a regulatory toxicology report submitted under REACH

Generate create a peer review record documenting the cross-validation findings for the machine-learning predictive model used in a regulatory toxicology report submitted under reach for Other Professional, Scientific, and Technical Services industry

Other Professional, Scientific, and Technical Services

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Primary REACH Dossier *

Click to upload or drag and drop

Allowed: ZIP, I6Z, PDF, XML

Max size: 100MB

Upload the complete registration dossier (IUCLID file or comprehensive PDF) containing the machine-learning toxicity model documentation submitted under REACH regulation (EC 1907/2006)

ML Model Validation Dataset *

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Allowed: XLSX, CSV, SDF, JSON

Max size: 50MB

Upload the independent validation dataset used for cross-validation, including raw experimental data, QSAR predictions, and associated metadata following OECD validation principles

Regulatory Context *

Select the specific REACH regulatory submission context that governs peer review requirements

Model Endpoint Categories *

Specify the toxicity endpoints that the ML model addresses, determining validation approach and data adequacy thresholds

Cross-Validation Methodology *

Define the validation approach following regulatory guidance and international standards

Reviewing Stakeholder Group *

Identify the primary audience determining review depth and presentation format

Compliance Standards *

Specify which quality and regulatory frameworks must be addressed in peer review findings

Critical Quality Thresholds *

Define specific acceptance criteria for model performance, statistical validity, and regulatory adequacy (e.g., R² ≥ 0.7, MAE ≤ 0.5 log units, concordance ≥ 80%)

Additional Regulatory Context

Provide any special circumstances, expedited timelines, confidential business information restrictions, or specific regulatory queries that influence peer review scope and findings presentation

Output Format Specification *

Select the required format and depth for peer review documentation and findings presentation

Create a Peer Review Record documenting the cross-validation findings for the machine-learning predictive model used in a regulatory toxicology report submitted under REACH

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