PubGuard — Multi-Head Scientific Publication Gatekeeper

Model Description

PubGuard is a lightweight, CPU-optimized document classifier that screens PDF text to determine whether it represents a genuine scientific publication. It rejects non-publications (posters, abstracts, review articles, flyers, invoices) before expensive downstream processing.

Three classification heads provide a multi-dimensional screening verdict:

1. Document type — Is this a paper, review, poster, abstract, or junk?
2. AI detection — Was this written by a human or generated by an LLM?
3. Toxicity — Does this contain toxic or offensive content?

Developed by Jamey O'Neill at the California Medical Innovations Institute (CalMI²).

Architecture

Three linear classification heads on frozen model2vec (potion-base-32M) embeddings:

┌─────────────┐
│  PDF text    │
└──────┬──────┘
       │
┌──────▼──────┐     ┌───────────────────┐
│  clean_text │────►│  model2vec encode  │──► emb ∈ R^512
└─────────────┘     └───────────────────┘
                            │
          ┌─────────────────┼─────────────────┐
          ▼                 ▼                  ▼
┌─────────────────┐ ┌──────────────┐ ┌──────────────┐
│ doc_type head    │ │ ai_detect    │ │ toxicity     │
│ [emb + 14 feats] │ │ head         │ │ head         │
│ → softmax(5)     │ │ → softmax(2) │ │ → softmax(2) │
└─────────────────┘ └──────────────┘ └──────────────┘

Each head is a single linear layer stored as a numpy .npz file (8–12 KB). Inference is pure numpy — no torch needed at prediction time.

The doc_type head additionally receives 14 structural features (section headings present, citation density, sentence length, etc.) concatenated with the embedding — these act as strong Bayesian priors.

Performance

doc_type Breakdown

Throughput

• 302 docs/sec single-document, 568 docs/sec batched (CPU only)
• 3.3ms per PDF screening — negligible pipeline overhead
• No GPU required

Gate Logic

Only scientific_paper passes the gate. Everything else — literature reviews, posters, standalone abstracts, junk — is blocked. The PubVerse pipeline processes original research publications only.

scientific_paper   →  ✅ PASS
literature_review  →  ❌ BLOCKED  (narrative/scoping reviews)
poster             →  ❌ BLOCKED  (classified, but not a publication)
abstract_only      →  ❌ BLOCKED
junk               →  ❌ BLOCKED

Note: Meta-analyses and systematic reviews are classified as scientific_paper (they are primary research). Only narrative and scoping reviews are classified as literature_review.

AI detection and toxicity are informational by default — reported but not blocking.

Usage

Python API

from pubguard import PubGuard

guard = PubGuard()
guard.initialize()

verdict = guard.screen("Introduction: We present a novel deep learning approach...")
print(verdict)
# {
#   'doc_type': {'label': 'scientific_paper', 'score': 0.994},
#   'ai_generated': {'label': 'human', 'score': 0.875},
#   'toxicity': {'label': 'clean', 'score': 0.999},
#   'pass': True
# }

Pipeline Integration (bash)

# Step 0 in run_pubverse_pipeline.sh:
PDF_TEXT=$(python3 -c "import fitz; d=fitz.open('$pdf'); print(' '.join(p.get_text() for p in d)[:8000])")
PUBGUARD_CODE=$(echo "$PDF_TEXT" | python3 pub_check/scripts/pubguard_gate.py 2>/dev/null)
# exit 0 = pass, exit 1 = reject

Installation

pip install git+https://github.com/jimnoneill/pubguard.git

With training dependencies:

pip install "pubguard[train] @ git+https://github.com/jimnoneill/pubguard.git"

Training Data

Trained on real datasets — zero synthetic data:

The poster class uses real scientific poster text from the posters.science corpus (28K+ verified posters from Zenodo & Figshare), extracted by PosterSentry.

The literature_review class uses a mix of open-access review article PDFs downloaded from OpenAlex and review abstracts as fallback. See the training data repo for full details.

Training

python scripts/train_pubguard.py --pdf-corpus /path/to/pdfs --n-per-class 15000

Training completes in ~1 minute on CPU. No GPU needed.

Model Specifications

Citation

@software{pubguard_2026,
  title = {PubGuard: Multi-Head Scientific Publication Gatekeeper},
  author = {O'Neill, James},
  year = {2026},
  url = {https://huggingface.co/jimnoneill/pubguard-classifier},
  note = {Part of the PubVerse + 42DeepThought pipeline}
}

License

This model is released under the MIT License.

Acknowledgments

• California Medical Innovations Institute (CalMI²)
• MinishLab for the model2vec embedding backbone
• FAIR Data Innovations Hub for the PosterSentry training data
• HuggingFace for model hosting infrastructure