Screen Submissions Before Peer Review
LLMs can generate citations that read as plausible but don't exist, and published papers with hallucinated references have already made it into the literature. Aiscern screens manuscripts at intake so editors and reviewers aren't the last line of defense.
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Hallucinated Citations Reach Publication
The AI content problem is getting harder to solve. Here's what professionals in academic research face every day.
AI-generated papers with hallucinated citations
LLMs generate plausible-sounding but non-existent citations. Published papers with hallucinated references undermine evidence-based research practices.
Synthetic datasets in empirical research
AI-generated datasets that do not reflect real-world phenomena can produce misleading research conclusions that persist in literature long after publication.
Peer review overload enabling AI submission proliferation
Overwhelmed reviewers cannot manually detect AI-generated papers. Automated pre-screening is now a practical necessity for high-volume journals.
Data fabrication in experiment logs and lab notes
AI assistance in generating experiment logs, results tables, and analysis text blurs the line between AI-assisted writing and outright fabrication.
How It Fits Your Workflow
Submit Manuscript
Upload a paper or connect the API to your submission portal.
Run Section-Level Analysis
The ensemble flags AI-generated paragraphs at the section level.
Review Editor Dashboard
See confidence scores across the submission queue before assigning reviewers.
How Aiscern Solves It
Our ensemble-based detection pipeline combines 8+ specialized models with a confidence threshold system.Learn about our methodology →
Research Paper Analysis
Full-document ensemble detection on academic papers with ≥96% AUC. Section-level confidence breakdown for targeted review.
Sentence-Level Heatmap
Identify which specific paragraphs and sections are AI-flagged — crucial for peer reviewers assessing partial AI use.
Batch Journal Submission Screening
Process entire submission batches. API integration available for journal management systems (OJS, ScholarOne, Editorial Manager).
Statistical Confidence Reporting
Detailed confidence intervals and model breakdown — the kind of methodological transparency academic contexts demand.
Audit Trail for IRB
Timestamped reports with scan IDs for institutional review board documentation and research integrity committees.
Data Privacy for Research
Submitted manuscripts are processed ephemerally. We do not train on your research content or retain it beyond the session.
ℹ️ Accuracy varies by content type and model generation date. Results are probabilistic — use alongside human judgment.See full benchmarks →
Real-World Use Cases
Journal Pre-Submission Screening
Challenge
A scientific journal needs every paper screened before reviewer assignment, without slowing the pipeline.
Action
The API is wired into the submission portal for automatic screening.
Institutional Research Integrity Audits
Challenge
A university research integrity office runs periodic audits of funded research outputs.
Action
Aiscern flags outputs for closer review by the standing committee.
Grant Application Verification
Challenge
A funding body needs project descriptions to reflect genuine investigator thinking.
Action
Proposals are screened during evaluation.
How Aiscern Compares to iThenticate
| Feature | Aiscern | iThenticate |
|---|---|---|
| Detects AI-generated text | Plagiarism-focused, limited AI detection | |
| Section-level heatmap | ||
| Journal management system API | ||
| Confidence uncertainty zone | ||
| Academic pricing tier |
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Frequently Asked Questions
How does Aiscern handle domain-specific scientific writing?
Scientific writing has domain-specific vocabularies and citation patterns. Our ensemble is trained on diverse academic corpora. Highly technical domain-specific writing may show wider confidence intervals. We recommend interpreting uncertain-zone scores (39–61%) with additional human review.
Can Aiscern detect AI use in only parts of a paper?
Yes — our sentence-level analysis highlights individual paragraphs and sentences that score above the AI threshold. This is particularly useful for papers where AI was used to generate introductions or discussion sections while methods and results are genuine.
Does detection work on preprints and arXiv-style papers?
Yes. Aiscern analyzes the text content of papers regardless of their publication status. PDF upload is supported — text is extracted and analyzed through the full ensemble.
What about legitimate AI-assisted writing tools used by researchers?
We distinguish between AI-assisted writing (grammar, clarity editing — typically scores 39–61%) and AI-generated content (≥62%). Many journals now require disclosure of AI assistance; Aiscern helps quantify the extent of that assistance.
Is there academic pricing available?
Yes. Educational institutions and non-commercial research organizations qualify for discounted plans. Contact us at /contact with your institutional email for academic pricing details.
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