Walid Al-Shaar

Novum Research & Innovation Group

Manuscript
Prescreening
Before Peer Review

A multi-signal integrity audit designed to detect fabricated references, AI-generated content, authorship anomalies, and image manipulation before your manuscript enters formal peer review.

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Research Integrity Context

Why Prescreening Matters

The infiltration of paper mills, AI-generated manuscripts, and fabricated citations into peer-reviewed journals represents a systemic threat. Single-signal checks are no longer sufficient.

Fabricated References Are Systematic

AI systems generate citations through predictive pattern matching, not actual reading. Fabricated citations are a characteristic feature of AI-generated content, not an occasional error.

Citation Integrity

Peer Review Has Been Compromised

Between 6.5% and 16.9% of peer review text already shows signs of AI modification. Paper mills specifically target journals with least-robust review pipelines, including special issues.

Systemic Threat

Authorship Fraud Is Commercially Organized

Paper mills sell authorship positions from $200 to over $2,000, with networks claiming to have facilitated more than 12,000 publications. Detection requires network-level analysis, not just text checks.

Authorship Integrity
Multi-Signal Framework

Five-Level Prescreening Protocol

Each level targets a distinct fraud mechanism invisible to the levels below. No single check is sufficient. All five are required for a defensible integrity audit.

L1

Text Similarity & Plagiarism Detection

Cross-database matching against published literature. Screens for verbatim copying, mosaic plagiarism, and paraphrased reuse. High volume, low cost.

High Volume
L2

AI-Language Detection

Flags LLM-generated text patterns including ChatGPT-specific vocabulary infiltration and tortured phrases produced by AI paraphrase tools that evade plagiarism detection.

Linguistic
L3

Authorship Network Analysis + FLP Index

Co-authorship clustering coefficient analysis, publication-age distribution screening, and repeat co-authorship pattern detection, supplemented by the First-author Leadership Publication (FLP) Index.

Network
L4

Reference Validity & Citation Verification

Four-stage verification: bibliographic cross-referencing (PubMed, Scopus, WoS, Google Scholar), DOI/ISBN validation, content-citation alignment assessment, and citation network anomaly detection.

Verification
L5

Image & Data Integrity Verification

Forensic screening for duplicated, manipulated, or AI-generated figures and data images. Includes statistical implausibility flags and EXIF metadata inconsistency analysis. Low volume, high precision.

Precision
Proprietary Methodology

FLP Index

N × (F%)²

N = total publications  •  F% = first-authorship rate

The First-Author Leadership Publication Index

Developed at NovumRIG, the FLP Index is a bibliometric screening instrument that detects authorship inflation and paper-mill buying patterns at the individual researcher level. Quadratic weighting of first-authorship rate amplifies the signal of low first-authorship far more effectively than raw publication counts.

A researcher with 200 publications but only 3% first-authorship yields an FLP score of 0.18. A genuine independent investigator with 50 publications at 80% first-authorship scores 32.0. Traditional h-index would obscure this distinction entirely.

Service Tiers

Prescreening Packages

Select the level of scrutiny appropriate to your journal tier and risk tolerance.

Essential

Baseline Screen

Entry-level integrity check suitable for non-indexed journals or conference proceedings.

  • L1: Text similarity & plagiarism
  • L2: AI-language detection
  • L3: Authorship network analysis
  • L4: Reference validity checking
  • L5: Image integrity verification
  • Summary report (PDF)
  • FLP Index audit
  • Post-revision re-check
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Standard ★ Most Requested

Full Integrity Audit

Complete five-level screening. Recommended for Scopus/WoS-indexed journals and IGI Global, Springer, or Elsevier submissions.

  • L1: Text similarity & plagiarism
  • L2: AI-language detection
  • L3: Authorship network analysis
  • L4: Reference validity checking
  • L5: Image integrity verification
  • Full structured report (PDF)
  • FLP Index audit per author
  • Post-revision re-check
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Premium

Comprehensive + Revision

All five levels plus a post-revision targeted re-screen. Ideal for high-impact journals or institutional compliance.

  • L1: Text similarity & plagiarism
  • L2: AI-language detection
  • L3: Authorship network analysis
  • L4: Reference validity checking
  • L5: Image integrity verification
  • Full structured report (PDF)
  • FLP Index audit per author
  • Post-revision re-check (1 round)
Request a Quote

Institutional packages for editorial boards available upon request. Turnaround: 3–7 working days.

Methodological Foundations

Research Basis

The prescreening framework and methodologies offered by NovumRIG are grounded in the following open-access publications by Dr. Walid Al-Shaar.

1

Al-Shaar, W. (2026). The Commercialization of Authorship: A Critical Analysis of Paper Mills, Sold Authorship, and the Erosion of Research Integrity. Zenodo.

zenodo.org/records/20077899
2

Al-Shaar, W. (2026). The First-author Leadership Publication Index: Addressing Authorship Inflation and Research Integrity in Academic Evaluation (Version 1.0). Zenodo.

zenodo.org/records/18653932
3

Al-Shaar, W. (2026). The Hidden Crisis: How Artificial Intelligence Is Undermining Scientific Literature Through Fabricated References and Compromised Research Integrity. Zenodo.

zenodo.org/records/20091673

Submit Your Manuscript for Prescreening

Each screening produces a signed, structured integrity report suitable for submission to editorial offices alongside your manuscript.

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