{
  "abstract": "Large Language Models (LLMs) can generate text describing scientific concepts, but the characteristics of these outputs remain poorly understood. We present a multi-dimensional characterization framework analyzing 9,666 outputs from seven models (GPT-4.1, GPT-5.2, GPT-5.5, o4-mini, Claude Sonnet 4.5, Claude Opus 4.5, and the open-weight Gemma-3-27B) across five scientific domains. Rather than making claims about creativity or novelty, we measure five independent dimensions: coherence, domain relevance, lexical profile, structural properties, and semantic position using four sentence embedding models spanning 2020–2024. After quality filtering (99.2% coherence, 99.9% domain relevance pass rates), we find that outputs exhibit graduate-level readability (median Flesch-Kincaid grade 16.3) and occupy semantic positions at the 83rd percentile of calibration distributions. All 39 metrics differ significantly across models (Kruskal-Wallis, p < 0.05), with structural properties showing the largest effects (ϵ2 = 0.35–0.54) and semantic position showing small-to-medium effects (ϵ2 ≈ 0.01–0.14). Dunn’s post-hoc tests with Bonferroni correction confirm that all model pairs differ on the top structural metrics (21/21 pairs for paragraph count, 20/21 for the next three). Centroid positions are robust to calibration sampling (bootstrap cosine similarity ≥ 0.99) and exceed a shuffled-domain baseline in 96.8–98.7% of cases; cross-model embedding consistency is moderate (Spearman ρ = 0.38–0.67) and is not explained by embedding dimensionality. Temperature effects replicate across two independent full-range models (GPT-4.1 and Gemma). This work provides calibrated measurements and validated methodology for future research without making interpretive claims about novelty.",
  "abstract_shown": true,
  "affiliations": [
    "Independent Researcher, Tehran, Iran"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jalil Ahmadpour"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eae5gj3e71c8s.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357892",
  "fulltext_url": null,
  "id": "eae5gj3e71c8s",
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    "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|Topic Modeling"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Creativity"
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      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Creativity"
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      "/Social sciences/Linguistics/Grammar/Phonology/Vocabulary"
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      "plos",
      "/Social sciences/Linguistics/Semantics"
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      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Creativity"
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  "openalex": "W7163993367",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357892&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/eae5gj3e71c8s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pone.0357892",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357892"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "mathematics",
      "origin": "plos:statistics",
      "subfield": "statistics"
    }
  ],
  "title": "Characterizing LLM scientific concept generation: A multi-dimensional measurement study",
  "title_text": "Characterizing LLM scientific concept generation: A multi-dimensional measurement study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/eae5gj3e71c8s",
  "venue": "PLOS One",
  "versions": []
}