{"id":"W2944651552","doi":"10.7202/1058474ar","title":"Storing Authenticity at the Surface and into the Depths: Securing Paper with Human- and Machine-Readable Devices1","year":2019,"lang":"en","type":"article","venue":"Intermédialités Histoire et théorie des arts des lettres et des techniques","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Covert; Readability; Computer science; Authentication (law); Space (punctuation); Computer security; Human–computer interaction; Internet privacy; Multimedia","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001162678,0.000380953,0.0003421617,0.00009366217,0.001332421,0.0007918041,0.0009291043,0.0001041177,0.00002782736],"category_scores_gemma":[0.00004818582,0.0002321634,0.00008573504,0.0002048915,0.001004837,0.001135972,0.0007916585,0.0003606981,0.00001072877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002759287,"about_ca_system_score_gemma":0.00004144191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002471839,"about_ca_topic_score_gemma":0.00824248,"domain_scores_codex":[0.9976981,0.0004145189,0.000423314,0.0006511614,0.0003903407,0.0004225913],"domain_scores_gemma":[0.9980875,0.0003892612,0.0002234518,0.001013962,0.0001615467,0.0001242465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001465627,0.0002767264,0.08050031,0.001266404,0.0003608524,0.0000815999,0.5327134,0.00001607562,0.0402245,0.2221637,0.001044563,0.1212054],"study_design_scores_gemma":[0.002151037,0.001902922,0.03222302,0.00457381,0.000354601,0.001462336,0.006564205,0.008755559,0.1166025,0.07462063,0.7468426,0.003946869],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9720904,0.002543913,0.009978395,0.002077475,0.0001684493,0.0007555269,0.000006743739,0.0005709099,0.01180814],"genre_scores_gemma":[0.9900438,0.0001384768,0.003131237,0.0009422998,0.00003320854,0.0000748384,0.000005566574,0.0000442155,0.005586322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.745798,"threshold_uncertainty_score":0.9999677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02351409369652513,"score_gpt":0.2762377002329093,"score_spread":0.2527236065363841,"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."}}