{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004035796,0.0005742895,0.0002756888,0.0004894639,0.0004286426,0.000851768,0.0007352067,0.0003947722,0.005487089],"category_scores_gemma":[0.000940804,0.0002142439,0.0005425471,0.0002452733,0.000419339,0.001701056,0.001132313,0.0005645143,0.0009707526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003217051,"about_ca_system_score_gemma":0.0003550541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008640856,"about_ca_topic_score_gemma":0.000895294,"domain_scores_codex":[0.9997615,0.00005905972,0.00002287415,0.00004552365,0.00008429374,0.00002676816],"domain_scores_gemma":[0.9998021,0.00008003825,0.00001856477,0.00002961627,0.00004272017,0.00002702039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004026538,0.0005459876,0.005146917,0.0007989469,0.000165928,0.001449586,0.002173137,0.04198697,0.122149,0.194722,0.01098856,0.6194704],"study_design_scores_gemma":[0.0001720086,0.0007547147,0.004587816,0.0003176811,0.0002655827,0.001373547,0.0005855691,0.5643227,0.06210127,0.06059794,0.3048045,0.0001166257],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03609906,0.0003437595,0.8913236,0.0003120718,0.0002373994,0.0004241695,0.00006238221,0.002996433,0.06820113],"genre_scores_gemma":[0.4125615,0.000992293,0.5417647,0.000365125,0.00009117171,0.0006367428,0.00021428,0.000308725,0.04306536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005487089,"threshold_uncertainty_score":0.01835614,"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."}}