AI transparency report
Show exactly how AI supported your screening
Generate methods text, review the recorded model configuration, measure human-AI agreement, and export screening decisions with the AI metadata available in your project.
Methods reporting
Turn project settings into a methods paragraph
Lumina generates editable methods text from the configuration recorded for your project. It identifies the embedding model, TF-IDF classifier, retraining interval, relevance-feedback settings, optional AI consultations, stopping rule, and final screening counts when those data are available.
The paragraph is a reporting aid, not a certification. Review it against your protocol, journal requirements, and the workflow your team actually followed before publication.
Title-abstract screening was performed using Lumina. Papers were initially ranked by semantic similarity using OpenAI text-embedding-3-small embeddings. After sufficient screening decisions, TF-IDF with Multinomial Naive Bayes contributed to queue prioritization and was periodically retrained. All final screening decisions were made by human reviewers.
Recorded configuration
See the components behind the ranking
The report exposes the configuration Lumina uses instead of replacing it with a generic “AI-powered” label.
- Embedding model
- text-embedding-3-small
- Classifier
- TF-IDF + MultinomialNB
- Relevance feedback
- Rocchio α 1.0 · β 0.5 · γ 0.25
- Human control
- AI recommendations are advisory
Audit CSV
Export decisions and available AI metadata
The audit export contains citation fields, screening order, reviewer decisions, exclusion reasons, notes, current paper scores, the project model version at export, and Pixel-Bot recommendation, confidence, and reasoning when the assistant was consulted.
- 01Connect human decisions to reviewers and timestamps.
- 02Inspect agreement between recorded AI recommendations and final human labels.
- 03Retain exclusion reasons, notes, and citation identifiers in one file.
Know what the report does not prove
A transparency report improves documentation, but it does not validate a stopping decision, guarantee recall, establish compliance, or prove that a review is methodologically rigorous. Those conclusions depend on the protocol, reviewer workflow, validation approach, and reporting requirements.