{
  "abstract": "Large language models such as ChatGPT, Gemini, Claude and DeepSeek now generate text that is routinely indistinguishable from human writing, yet virtually every deployed detector answers only one binary question: human or AI. This is the wrong question for the settings that matter most. Digital forensics needs to know which model produced a text; academic-integrity adjudication needs to know whether a human was involved at all; and neither can be answered by a single probability, least of all for the hybrid human-AI drafts that now dominate real-world writing. This paper addresses that gap with SiBot, an end-to-end framework that reformulates AI-text detection as fine-grained multi-class source attribution and carries it through to public deployment. We construct two purpose-built corpora using a controlled bank of 3,000 prompts spanning six subject domains and five task types: a 66,000-sample corpus covering 22 AI sources, and a 30,000-sample corpus covering 10 classes that explicitly models five pure sources and four human-AI collaboration workflows; both are publicly released as the TXD-22 benchmark dataset. On the 22-class corpus we benchmark twelve feature-classifier combinations and find that TF-IDF with Random Forest attains 68.2% accuracy and an AUC of 0.742. On the 10-class corpus we benchmark fifteen classical, deep-learning and fine-tuned transformer baselines, and propose a hybrid architecture that fuses sparse TF-IDF lexical features with DeBERTa contextual embeddings at the feature level before Random Forest classification. The proposed hybrid attains 92.38% accuracy, 92.41% precision, 92.38% recall and 92.31% F1 under prompt-aware 10-fold cross-validation, a 17.73-point gain over the strongest fine-tuned transformer (DeBERTa, 74.65%) and a 34.30-point gain over TF-IDF with Random Forest alone, with non-overlapping 95% confidence intervals. An ablation confirms that both branches are necessary, and SHAP and LIME analyses expose the lexical evidence behind individual decisions. Because the 10-class hybrid is both substantially more accurate and interpretable, it is the model served by the live SiBot platform; the 22-class model is reported here as a research benchmark, and extending the deployment to all 22 classes is left to future work. In a controlled three-way benchmark against QuillBot, Turnitin, ZeroGPT and TextGuard, SiBot is the only system that names the generating model of AI text (Gemini, 96.7% confidence) and the only one that recognises hybrid provenance (97.7% confidence, against verdicts ranging from 0% to 76% among the commercial tools on the identical input).",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science and Engineering, Dhaka University of Engineering and Technology, Gazipur, Bangladesh"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Md. Sadiq Iqbal"
    },
    {
      "name": "Mohammod Abul Kashem"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9xq8sty85s3gx.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357405",
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  "id": "9xq8sty85s3gx",
  "issns": [
    "1932-6203"
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Authorship Attribution and Profiling"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence"
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      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
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      "/Engineering and technology/Mechanical engineering/Rotors"
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      "/Physical sciences/Physics/Condensed matter physics/Magnetism/Magnetic fields"
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      "plos",
      "/Social sciences/Law and legal sciences/Administrative law"
    ],
    [
      "plos",
      "/Social sciences/Law and legal sciences/Contract law"
    ],
    [
      "plos",
      "/Social sciences/Law and legal sciences/Environmental law"
    ],
    [
      "plos",
      "/Social sciences/Linguistics/Semantics"
    ]
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  "openalex": "W7211960474",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357405&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9xq8sty85s3gx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0357405",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357405"
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  "subjects": [
    {
      "field": "physics",
      "origin": "plos:condensed matter physics",
      "subfield": "condensed-matter"
    }
  ],
  "title": "SiBot: A hybrid framework for fine-grained semantic attribution of AI-generated text and its production deployment",
  "title_text": "SiBot: A hybrid framework for fine-grained semantic attribution of AI-generated text and its production deployment",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/9xq8sty85s3gx",
  "venue": "PLOS One",
  "versions": []
}