20.500.12556/DiRROS-24109 Statistical learning improves classification of limestone provenance Determining the lithostratigraphic provenance of limestone artefacts is challenging. We addressed the issue by analysing Roman stone artefacts, where previously traditionalpetrological methods failed to identify the provenance of 72% of the products due to the predominance of micrite limestone. We applied statistical classification methods to 15 artefacts using linear discriminant analysis, decision trees, random forest, and support vector machines. The latter achieved the highest accuracy, with 73% of the samples classified to the same stratigraphic member as determined by the expert. We improved classification reliability and evaluated it by aggregating the results of different classifiers for each stone product. Combining aggregated results with additional evidence from paleontological data or precise optical microscopy leads to successful provenance determination. After a few samples were reassigned in this procedure, a support vector machine correctly classified 87% of the samples. Strontium isotope ratios (87Sr/86Sr) proved particularly effective as provenance indicators. We successfully assigned all stone products to local sources across four lithostratigraphic members, thereby confirming local patterns of stone use by Romans. We provide guidance for future use of statistical learning in provenance determination. Our integrated approach, combining geological and statistical expertise, provides a robust framework for challenging provenance determination. antiquity micrite limestone machine learning statistics R regio X artefacts Ig area true false true Angleški jezik Ni določen Neznano 2025-11-13 04:03:01 2025-11-17 11:13:55 2025-11-18 03:43:30 0000-00-00 00:00:00 2025 0 0 21 str. no. 11 vol. 8 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2025-11-06 551.1/.4 2571-9408 10.3390/heritage8110464 256913155 heritage-08-00464.pdf heritage-08-00464.pdf 1 AE82AF4D61334B4AC53F889F1F55BF7B 1c04877e46828cacfb92f27e4a6ff3aed4f194d25e42bbb176e3fd149de8772f 5393bd1f-c39e-11f0-94a7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=35904 Geološki zavod Slovenije 0 0 0