{"id":"W2890555171","doi":"10.1145/3236495","title":"Persona","year":2018,"lang":"en","type":"article","venue":"Computers in entertainment","topic":"Persona Design and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul","keywords":"Persona; Computer science; Avatar; Support vector machine; Artificial intelligence; Facial expression; Set (abstract data type); Expression (computer science); Feature selection; Action (physics); Feature (linguistics); Face (sociological concept); Human–computer interaction; Pattern recognition (psychology); Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006367374,0.001258157,0.0006119253,0.002055952,0.001394946,0.002154658,0.001336387,0.001344166,0.0381056],"category_scores_gemma":[0.001823739,0.0005808904,0.00135058,0.0009222571,0.0008063951,0.002427169,0.002414429,0.0009554187,0.02620775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005236813,"about_ca_system_score_gemma":0.0006159552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002068386,"about_ca_topic_score_gemma":0.002335857,"domain_scores_codex":[0.9985201,0.0002045833,0.00006532435,0.0006773791,0.0004431769,0.00008944736],"domain_scores_gemma":[0.9994918,0.00006535027,0.00003578585,0.000209217,0.0001554431,0.00004233922],"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":[0.0001935633,0.00007408851,0.001863254,0.0003775548,0.0001022615,0.0002498406,0.000589376,0.001753779,0.0122306,0.06457147,0.03519234,0.8828017],"study_design_scores_gemma":[0.00004125877,0.0001725762,0.005056097,0.0002028946,0.0001246061,0.004536236,0.0006942543,0.04178987,0.02366362,0.03543149,0.8881411,0.0001460216],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006545933,0.001546706,0.9154767,0.0004146663,0.0005884261,0.0003351124,0.0008199746,0.007654083,0.06661835],"genre_scores_gemma":[0.1148085,0.001614637,0.7550557,0.0006613073,0.000262653,0.000490741,0.003229386,0.001740578,0.1221365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0381056,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279793565446876,"score_gpt":0.2494737101369993,"score_spread":0.2366757744825305,"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."}}