{"id":"W3122166314","doi":"10.3390/app10030923","title":"Communication with Self-Growing Character to Develop Physically Growing Robot Toy Agent","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Institute for Information and Communications Technology Promotion; Korean Intellectual Property Office; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; Seoul National University","keywords":"Robot; Human–computer interaction; Character (mathematics); Computer science; Psychology; Expression (computer science); Artificial intelligence; Mathematics","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":[0.00127709,0.0003987637,0.0001491576,0.0003820476,0.001347876,0.002137858,0.0004717291,0.0007415604,0.005734144],"category_scores_gemma":[0.003084629,0.0002014645,0.000288305,0.0001204694,0.001751404,0.001771134,0.001983145,0.001054531,0.001088971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007736993,"about_ca_system_score_gemma":0.0006989028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005622408,"about_ca_topic_score_gemma":0.001149354,"domain_scores_codex":[0.9987205,0.0008205935,0.00003886527,0.0001188734,0.0002119916,0.00008916804],"domain_scores_gemma":[0.9985806,0.0005109655,0.0002280286,0.0001633354,0.000222845,0.0002942342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003203578,0.001828523,0.03432181,0.001146566,0.00006650821,0.006399874,0.4096715,0.002175599,0.129447,0.1438015,0.01526697,0.2555539],"study_design_scores_gemma":[0.0000764603,0.002334966,0.04480643,0.0005588353,0.0001119858,0.01312701,0.1747261,0.01201902,0.04833141,0.0182537,0.6854479,0.0002061542],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7709752,0.0005288008,0.07296244,0.002980154,0.0001897875,0.0003565717,0.00005487251,0.0004237181,0.1515285],"genre_scores_gemma":[0.9222744,0.0002934765,0.03968593,0.0003507065,0.00002034131,0.0002626152,0.00004930959,0.00008178614,0.03698146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005734144,"threshold_uncertainty_score":0.01918262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06032415128560029,"score_gpt":0.3380452510993527,"score_spread":0.2777210998137524,"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."}}