Ethics & Scientific Integrity.
Defining the boundary of high-rigor autonomous adjudication and human-led research excellence.
01. The Peer-Audit Standard
Neural Review employs a hybrid Peer-Audit Standard. While autonomous nodes perform initial rigor screening (methodology validation, citation network analysis, and AI-signature detection), final certification is always granted by qualified human experts.
This dual-layer approach ensures that while mechanical errors are eliminated, the nuanced scientific advancement and societal impact are judged by peers with deep subject expertise.
Conflict Resolution
"Reviewers must recuse themselves from any audit involving direct collaborators, institutional peers, or direct competitors within a 5-year window."
Data Transparency
"Authors are expected to provide verifiable data substrates. Our nodes verify the integrity of figures and supplemental datasets via cryptographic hashing."
02. Autonomous Bias Mitigation
Our adjudication nodes are audited quarterly for "Semantic Neutrality". We actively monitor for regional, institutional, or linguistic biases in the automated pre-screening phase to ensure a globally equitable submission flow.
Ethics Protocol v5.2 // Full Registry Certification