{
  "abstract": "Published models that classify cannabis products from cannabinoid and terpene profiles report near-perfect accuracy: above 95% for resin type, 100% for chemovar class. These figures come from random cross-validation or Kennard-Stone partitioning, which hold out no plant, producer or laboratory. We quantify the cost on a task with explicit group structure. On 33,229 public laboratory-result rows for cannabis concentrates that name a producer, nearly all from Nevada, each with a source-declared extraction category (non-solvent against solvent-based), a gradient-boosted classifier scores 78.5% balanced accuracy and a ROC AUC of 0.89 under random five-fold cross-validation. Holding out the producer leaves 54.0% and 0.62. A signal survives: the pooled held-out AUC is 0.61, with a producer-level bootstrap interval of 0.535 to 0.698. But the reported model's pooled balanced accuracy does not separate from chance (51.6%, interval 47.3 to 56.6); it keeps about 30% of its above-chance AUC, and an unweighted logistic regression 41 to 53%. A designed experiment shows the same direction: with the random split averaged over 200 seeds, 162 HPLC-assayed samples fall from 54.3% to 38.9% under leave-one-variety-out with gradient boosting and from 51.8% to 46.3% with logistic regression, against 33.3% chance. In the market data the same profiles identify the producer at 67.1% against a 14.4% baseline and the laboratory at 92.3% against 40.3%. We conclude that the declared extraction category is only weakly recoverable from these analyte panels for an unseen producer, and that a random split overstates it by 8 to 26 points of balanced accuracy depending on the model and subset. We did not rerun the published studies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Arthur Gonçalves Breguez",
      "orcid": "0009-0005-8551-731X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/12xq0c0g1e9cf.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010167/v1",
  "fulltext_url": null,
  "id": "12xq0c0g1e9cf",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010167/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1602|Spectroscopy and Chemometric Analyses"
    ]
  ],
  "openalex": "W7221163875",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010167/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/12xq0c0g1e9cf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15010167",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010167/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1602|Spectroscopy and Chemometric Analyses",
      "subfield": "analytical"
    }
  ],
  "title": "Group-aware validation sharply reduces the apparent accuracy of extraction-category classification in public cannabis laboratory data",
  "title_text": "Group-aware validation sharply reduces the apparent accuracy of extraction-category classification in public cannabis laboratory data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/12xq0c0g1e9cf",
  "venue": "ChemRxiv",
  "versions": []
}