{"id":"W4323781198","doi":"10.2298/csis220719021z","title":"Using artificial intelligence assistant technology to develop animation games on IoT","year":2023,"lang":"en","type":"article","venue":"Computer Science and Information Systems","topic":"Human Motion and Animation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Animation; Flexibility (engineering); Adaptation (eye); Artificial intelligence; Modular design; Computer animation; Human–computer interaction; Skeletal animation; Object (grammar); Multimedia; Computer facial animation; Programming language; Computer graphics (images)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005005208,0.00006995383,0.00007665805,0.0008682106,0.0001818059,0.0003415755,0.0001241571,0.00003651124,0.000001872213],"category_scores_gemma":[0.00004797263,0.00006554937,0.00000592041,0.002375404,0.00004642195,0.0009641317,0.00004256061,0.00004764067,0.000377981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009365769,"about_ca_system_score_gemma":0.0000403419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001245382,"about_ca_topic_score_gemma":3.890842e-7,"domain_scores_codex":[0.9991993,0.000006694695,0.0002876455,0.00008734061,0.0002655382,0.0001534712],"domain_scores_gemma":[0.9995294,0.00001028016,0.00004033262,0.00009640268,0.0002692851,0.00005432598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002558441,0.000005025425,0.00004090783,0.000111012,0.000004006933,6.72421e-7,0.003058114,0.4028089,0.004643134,0.1759044,0.0007062698,0.412715],"study_design_scores_gemma":[0.00001676004,0.00003187107,0.000930797,0.00006694102,6.230517e-7,0.000005342133,0.0003631434,0.9910697,0.001829677,0.0001202346,0.005477938,0.00008692282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2587932,0.00000478332,0.7392884,0.0001112437,0.0006140745,0.0001623317,0.000002000625,0.000388542,0.0006353247],"genre_scores_gemma":[0.9965011,0.000004348969,0.003332025,0.00008971021,0.00005358286,0.000008987928,0.000003586121,0.000003164698,0.000003476438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7377079,"threshold_uncertainty_score":0.4858307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05344078124181206,"score_gpt":0.2873702605147612,"score_spread":0.2339294792729492,"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."}}