{"id":"W1591372713","doi":"10.1109/gem.2014.7048098","title":"Introducing the biometric storyboards tool for games user research","year":2014,"lang":"en","type":"article","venue":"","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Biometrics; Unification; Human–computer interaction; Component (thermodynamics); Field (mathematics); Data science; Multimedia; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003429871,0.00008029538,0.00009300992,0.001085403,0.0003675901,0.0001860323,0.00115909,0.00007140112,0.00004308932],"category_scores_gemma":[0.001052227,0.00005171594,0.00003597249,0.002519119,0.0001563832,0.0004237265,0.0003371031,0.0003286589,0.0001614265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001137397,"about_ca_system_score_gemma":0.00003783651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002182632,"about_ca_topic_score_gemma":0.000009131269,"domain_scores_codex":[0.9986522,0.0001606075,0.0001709824,0.0003741714,0.000310725,0.0003313478],"domain_scores_gemma":[0.9974658,0.0009075291,0.00005522954,0.0008575333,0.0007001357,0.00001373119],"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":[0.000005601073,0.00002645401,0.000258763,0.000006093787,0.00001485916,2.468985e-7,0.00024413,0.00001080033,0.007183041,0.8396931,0.05089881,0.1016582],"study_design_scores_gemma":[0.000456021,0.000515978,0.005247043,0.00001327254,0.000003698961,0.00001379501,0.0001929741,0.05777101,0.06580417,0.0289731,0.8407651,0.000243892],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05524973,0.00001610083,0.9329485,0.006884333,0.0007346928,0.0003135304,3.700608e-7,0.0002117304,0.003641019],"genre_scores_gemma":[0.9307642,0.000001573376,0.06247071,0.0006318116,0.0003068028,0.0001278448,8.576928e-7,0.000009891288,0.005686319],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8755144,"threshold_uncertainty_score":0.2827243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06069730899174428,"score_gpt":0.3723209171888281,"score_spread":0.3116236081970838,"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."}}