{"id":"W3111519450","doi":"10.16995/dm.91","title":"A TEI Customization for Paper and Watermarks Descriptions","year":2020,"lang":"en","type":"article","venue":"Digital Medievalist","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personalization; Computer science; Context (archaeology); XML; Key (lock); World Wide Web; Order (exchange); Information retrieval; Computer security; History","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00004748802,0.0001246769,0.0001351134,0.00003744524,0.0002570177,0.001809216,0.00009412146,0.0000276614,0.0004084987],"category_scores_gemma":[0.0001523251,0.0001032474,0.00006401596,0.00001674488,0.0002078952,0.00173898,0.00004422214,0.00006465041,0.00004374118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001242626,"about_ca_system_score_gemma":0.00001200718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007469241,"about_ca_topic_score_gemma":0.00006147051,"domain_scores_codex":[0.9993173,0.000006785569,0.0001815509,0.0001855193,0.0001246949,0.0001841557],"domain_scores_gemma":[0.9995893,0.0000552912,0.00003820741,0.00007613195,0.0001017187,0.0001393047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002552422,0.0001392974,0.0004335988,0.0003222887,0.000101926,0.00001241135,0.07224559,0.000003970921,0.0001296733,0.7462786,0.1386043,0.04147314],"study_design_scores_gemma":[0.000390396,0.0001314071,0.0000387344,0.00001876773,0.00001919431,0.000001964588,0.003345285,0.0001938885,0.00001583674,0.003169555,0.9925084,0.000166563],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07923134,0.0003272778,0.0002401965,0.006116553,0.0007047843,0.0006713941,0.001414359,0.0002877682,0.9110063],"genre_scores_gemma":[0.9830871,0.00000750268,0.00002387916,0.002480255,0.0007499374,0.00003746598,0.0003214798,0.00002434022,0.01326806],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9038557,"threshold_uncertainty_score":0.999227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06283823397461064,"score_gpt":0.2226755910128268,"score_spread":0.1598373570382162,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}