{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009957414,0.001343542,0.0008397107,0.005292744,0.001748224,0.007862384,0.002544103,0.001820312,0.02021476],"category_scores_gemma":[0.03091159,0.001343977,0.001709893,0.004872328,0.002516168,0.01126828,0.007514474,0.004243749,0.01665335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002574173,"about_ca_system_score_gemma":0.003953475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002498,"about_ca_topic_score_gemma":0.001800089,"domain_scores_codex":[0.9871988,0.002491187,0.003388257,0.001425235,0.0047713,0.000725252],"domain_scores_gemma":[0.9695103,0.004504238,0.002157453,0.01489304,0.00804371,0.0008912754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007419394,0.0003755963,0.003549829,0.001345175,0.00007787461,0.001906295,0.007896051,0.003691251,0.04709393,0.320983,0.1189393,0.4933998],"study_design_scores_gemma":[0.00003596734,0.0001151195,0.0008705069,0.0004612939,0.00004476379,0.001657487,0.0009557932,0.006106943,0.02861734,0.01883113,0.942142,0.0001616515],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01296983,0.0006505181,0.8664135,0.00171727,0.001125931,0.002455959,0.00877354,0.03684957,0.06904383],"genre_scores_gemma":[0.09916863,0.001800178,0.7765578,0.001616877,0.0007267465,0.002730272,0.02403746,0.01907459,0.07428744],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02021476,"threshold_uncertainty_score":0.06762511,"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."}}