{"id":25669,"date":"2025-08-18T13:37:45","date_gmt":"2025-08-18T11:37:45","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=25669"},"modified":"2025-08-19T10:22:24","modified_gmt":"2025-08-19T08:22:24","slug":"stylometry-recognizes-human-and-llm-generated-texts-in-short-samples","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/stylometry-recognizes-human-and-llm-generated-texts-in-short-samples\/","title":{"rendered":"Stylometry recognizes human and LLM-generated texts in short samples"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-karol-nbsp-przystalski-nbsp-jan-k-nbsp-argasinski-nbsp-iwona-nbsp-grabska-gradzinska-nbsp-jeremi-k-nbsp-ochab\">Karol&nbsp;Przystalski,&nbsp;Jan K.&nbsp;Argasi\u0144ski,&nbsp;Iwona&nbsp;Grabska-Gradzi\u0144ska,&nbsp;Jeremi K.&nbsp;Ochab<\/h2>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">A recent study co-authored by <a href=\"https:\/\/sano.science\/people\/jan-argasinski\/\">Jan K. Argasi\u0144ski <\/a>from the Computational Neuroscience group at the Sano Centre for Computational Medicine investigates the potential of stylometry \u2014 a technique traditionally applied in authorship attribution \u2014 to differentiate between human-written content and texts produced by Large Language Models (LLMs).<\/p>\n\n\n\n<p class=\" eplus-wrapper\">The researchers developed a benchmark dataset sourced from Wikipedia, featuring:<\/p>\n\n\n<ul class=\"wp-block-list eplus-wrapper eplus-styles-uid-6362f5\">\n<li class=\" eplus-wrapper\">Summaries authored by humans<\/li>\n\n\n\n<li class=\" eplus-wrapper\">Summaries generated by various LLMs (GPT-3.5\/4, LLaMa 2\/3, Orca, Falcon)<\/li>\n\n\n\n<li class=\" eplus-wrapper\">Summaries altered by automatic summarisation tools (T5, BART, Gensim, Sumy)<\/li>\n\n\n\n<li class=\" eplus-wrapper\">Texts reworded using paraphrasing systems (Dipper, T5)<\/li>\n<\/ul>\n\n\n<p class=\" eplus-wrapper\">By applying decision trees and LightGBM classifiers, and incorporating both manually crafted features (via the StyloMetrix toolkit) and n-gram-based stylometric indicators, the models achieved notable results:<\/p>\n\n\n<ul class=\"wp-block-list eplus-wrapper eplus-styles-uid-714481\">\n<li class=\" eplus-wrapper\">Up to 0.87 Matthews correlation coefficient (MCC) in a multi-class (7-way) classification task<\/li>\n\n\n\n<li class=\" eplus-wrapper\">Between 0.79 and 1.0 accuracy in binary classification, with detection of GPT-4 texts reaching 98% accuracy on balanced datasets<\/li>\n<\/ul>\n\n\n<p class=\" eplus-wrapper\">Interpretability analysis showed that LLM-generated texts tend to be more grammatically uniform and exhibit distinctive lexical patterns compared to human writing. These findings demonstrate that, despite the growing sophistication of AI-generated text, distinguishing between machine and human authorship remains feasible \u2014 especially for structured formats like encyclopaedia entries.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"260\" src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-1024x260.jpg\" alt=\"\" class=\"wp-image-25671\" srcset=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-1024x260.jpg 1024w, https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-300x76.jpg 300w, https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-768x195.jpg 768w, https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-1536x391.jpg 1536w, https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-2048x521.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\" eplus-wrapper\"><em>Fig. 1.&nbsp;Explanations for binary classification between the Wikipedia and GPT-4.<\/em><\/p>\n\n\n\n<p class=\" eplus-wrapper\">Source:&nbsp;<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417425026181#fig0002\" target=\"_blank\" rel=\"noreferrer noopener\">www.sciencedirect.com\/science\/article\/pii\/S0957417425026181#fig0002<\/a><\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417425026181?via%3Dihub\" target=\"_self\"  class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Stylometry recognizes human and LLM-generated texts in short samples<\/strong><br><strong>Authors<\/strong>:&nbsp;Karol&nbsp;Przystalski,&nbsp;Jan K.&nbsp;Argasi\u0144ski,&nbsp;Iwona&nbsp;Grabska-Gradzi\u0144ska,&nbsp;Jeremi K.&nbsp;Ochab<br><strong>DOI<\/strong>:&nbsp;<a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2025.129001\" target=\"_blank\" rel=\"noreferrer noopener\">10.1016\/j.eswa.2025.129001<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Karol&nbsp;Przystalski,&nbsp;Jan K.&nbsp;Argasi\u0144ski,&nbsp;Iwona&nbsp;Grabska-Gradzi\u0144ska,&nbsp;Jeremi K.&nbsp;Ochab A recent study co-authored by Jan K. Argasi\u0144ski from the Computational Neuroscience group at the Sano Centre for Computational Medicine investigates the potential of stylometry \u2014 a technique traditionally applied in authorship attribution \u2014 to differentiate between human-written content and texts produced by Large Language Models (LLMs). The researchers developed a benchmark [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-25669","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-neuroscience"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Stylometry recognizes human and LLM-generated texts in short samples - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/stylometry-recognizes-human-and-llm-generated-texts-in-short-samples\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Stylometry recognizes human and LLM-generated texts in short samples\" \/>\n<meta property=\"og:description\" content=\"Karol&nbsp;Przystalski,&nbsp;Jan K.&nbsp;Argasi\u0144ski,&nbsp;Iwona&nbsp;Grabska-Gradzi\u0144ska,&nbsp;Jeremi K.&nbsp;Ochab A recent study co-authored by Jan K. Argasi\u0144ski from the Computational Neuroscience group at the Sano Centre for Computational Medicine investigates the potential of stylometry \u2014 a technique traditionally applied in authorship attribution \u2014 to differentiate between human-written content and texts produced by Large Language Models (LLMs). The researchers developed a benchmark [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/stylometry-recognizes-human-and-llm-generated-texts-in-short-samples\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2025-08-19T08:22:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"651\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/stylometry-recognizes-human-and-llm-generated-texts-in-short-samples\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/stylometry-recognizes-human-and-llm-generated-texts-in-short-samples\\\/\",\"name\":\"Stylometry recognizes human and LLM-generated texts in short samples - 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Argasi\u0144ski from the Computational Neuroscience group at the Sano Centre for Computational Medicine investigates the potential of stylometry \u2014 a technique traditionally applied in authorship attribution \u2014 to differentiate between human-written content and texts produced by Large Language Models (LLMs). 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(max-width:600px){.eplus-styles-uid-714481{list-style-type:}}.eplus-styles-uid-714481:hover{list-style-type:}"}},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-fpdlUo","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-karol-nbsp-przystalski-nbsp-jan-k-nbsp-argasinski-nbsp-iwona-nbsp-grabska-gradzinska-nbsp-jeremi-k-nbsp-ochab\">Karol&nbsp;Przystalski,&nbsp;Jan K.&nbsp;Argasi\u0144ski,&nbsp;Iwona&nbsp;Grabska-Gradzi\u0144ska,&nbsp;Jeremi K.&nbsp;Ochab<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-karol-nbsp-przystalski-nbsp-jan-k-nbsp-argasinski-nbsp-iwona-nbsp-grabska-gradzinska-nbsp-jeremi-k-nbsp-ochab\">Karol&nbsp;Przystalski,&nbsp;Jan K.&nbsp;Argasi\u0144ski,&nbsp;Iwona&nbsp;Grabska-Gradzi\u0144ska,&nbsp;Jeremi K.&nbsp;Ochab<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-keL0bT","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-sG0pIN","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">A recent study co-authored by <a href=\"https:\/\/sano.science\/people\/jan-argasinski\/\">Jan K. Argasi\u0144ski <\/a>from the Computational Neuroscience group at the Sano Centre for Computational Medicine investigates the potential of stylometry \u2014 a technique traditionally applied in authorship attribution \u2014 to differentiate between human-written content and texts produced by Large Language Models (LLMs).<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">A recent study co-authored by <a href=\"https:\/\/sano.science\/people\/jan-argasinski\/\">Jan K. Argasi\u0144ski <\/a>from the Computational Neuroscience group at the Sano Centre for Computational Medicine investigates the potential of stylometry \u2014 a technique traditionally applied in authorship attribution \u2014 to differentiate between human-written content and texts produced by Large Language Models (LLMs).<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-dj0jA9","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The researchers developed a benchmark dataset sourced from Wikipedia, featuring:<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The researchers developed a benchmark dataset sourced from Wikipedia, 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class=\" eplus-wrapper\">Summaries authored by humans<\/li>\n","innerContent":["\n<li class=\" eplus-wrapper\">Summaries authored by humans<\/li>\n"]},{"blockName":"core\/list-item","attrs":{"epAnimationGeneratedClass":"edplus_anim-5lm2GJ","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<li class=\" eplus-wrapper\">Summaries generated by various LLMs (GPT-3.5\/4, LLaMa 2\/3, Orca, Falcon)<\/li>\n","innerContent":["\n<li class=\" eplus-wrapper\">Summaries generated by various LLMs (GPT-3.5\/4, LLaMa 2\/3, Orca, Falcon)<\/li>\n"]},{"blockName":"core\/list-item","attrs":{"epAnimationGeneratedClass":"edplus_anim-k7Ifax","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<li class=\" eplus-wrapper\">Summaries altered by automatic summarisation tools (T5, BART, Gensim, Sumy)<\/li>\n","innerContent":["\n<li class=\" eplus-wrapper\">Summaries altered by automatic summarisation tools (T5, BART, Gensim, 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class=\" eplus-wrapper\">Up to 0.87 Matthews correlation coefficient (MCC) in a multi-class (7-way) classification task<\/li>\n","innerContent":["\n<li class=\" eplus-wrapper\">Up to 0.87 Matthews correlation coefficient (MCC) in a multi-class (7-way) classification task<\/li>\n"]},{"blockName":"core\/list-item","attrs":{"epAnimationGeneratedClass":"edplus_anim-RwUbiW","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<li class=\" eplus-wrapper\">Between 0.79 and 1.0 accuracy in binary classification, with detection of GPT-4 texts reaching 98% accuracy on balanced datasets<\/li>\n","innerContent":["\n<li class=\" eplus-wrapper\">Between 0.79 and 1.0 accuracy in binary classification, with detection of GPT-4 texts reaching 98% accuracy on balanced datasets<\/li>\n"]}],"innerHTML":"<ul class=\"wp-block-list eplus-wrapper eplus-styles-uid-714481\">\n\n<\/ul>","innerContent":["\n<ul class=\"wp-block-list eplus-wrapper\">",null,"\n\n",null,"<\/ul>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-sG0pIN","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Interpretability analysis showed that LLM-generated texts tend to be more grammatically uniform and exhibit distinctive lexical patterns compared to human writing. These findings demonstrate that, despite the growing sophistication of AI-generated text, distinguishing between machine and human authorship remains feasible \u2014 especially for structured formats like encyclopaedia entries.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Interpretability analysis showed that LLM-generated texts tend to be more grammatically uniform and exhibit distinctive lexical patterns compared to human writing. These findings demonstrate that, despite the growing sophistication of AI-generated text, distinguishing between machine and human authorship remains feasible \u2014 especially for structured formats like encyclopaedia entries.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-3CwJPj","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/image","attrs":{"id":25671,"sizeSlug":"large","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-D7P1CU","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-1024x260.jpg\" alt=\"\" class=\"wp-image-25671\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-1024x260.jpg\" alt=\"\" class=\"wp-image-25671\"\/><\/figure>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-FrkgCl","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><em>Fig. 1.&nbsp;Explanations for binary classification between the Wikipedia and GPT-4.<\/em><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><em>Fig. 1.&nbsp;Explanations for binary classification between the Wikipedia and GPT-4.<\/em><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-FrkgCl","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Source:&nbsp;<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417425026181#fig0002\" target=\"_blank\" rel=\"noreferrer noopener\">www.sciencedirect.com\/science\/article\/pii\/S0957417425026181#fig0002<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Source:&nbsp;<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417425026181#fig0002\" target=\"_blank\" rel=\"noreferrer noopener\">www.sciencedirect.com\/science\/article\/pii\/S0957417425026181#fig0002<\/a><\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-PeMgAz","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417425026181?via%3Dihub","button_style":"primary","target":"_self","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-PeMgAz","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-GAnn1Y","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Stylometry recognizes human and LLM-generated texts in short samples<\/strong><br><strong>Authors<\/strong>:&nbsp;Karol&nbsp;Przystalski,&nbsp;Jan K.&nbsp;Argasi\u0144ski,&nbsp;Iwona&nbsp;Grabska-Gradzi\u0144ska,&nbsp;Jeremi K.&nbsp;Ochab<br><strong>DOI<\/strong>:&nbsp;<a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2025.129001\" target=\"_blank\" rel=\"noreferrer noopener\">10.1016\/j.eswa.2025.129001<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Stylometry recognizes human and LLM-generated texts in short samples<\/strong><br><strong>Authors<\/strong>:&nbsp;Karol&nbsp;Przystalski,&nbsp;Jan K.&nbsp;Argasi\u0144ski,&nbsp;Iwona&nbsp;Grabska-Gradzi\u0144ska,&nbsp;Jeremi K.&nbsp;Ochab<br><strong>DOI<\/strong>:&nbsp;<a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2025.129001\" target=\"_blank\" rel=\"noreferrer noopener\">10.1016\/j.eswa.2025.129001<\/a><\/p>\n"]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/25669","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":11,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/25669\/revisions"}],"predecessor-version":[{"id":25683,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/25669\/revisions\/25683"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=25669"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=25669"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=25669"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}