{"id":"W4385005568","doi":"","title":"Visual Learning for Reaching and Body-Schema with Gain-Field Networks","year":2018,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Action Observation and Synchronization","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"NeuroDevNet","funders":"","keywords":"Computer science; Schema (genetic algorithms); Body schema; Artificial intelligence; Human–computer interaction; Machine learning; Psychology; Neuroscience","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":[0.001966616,0.0001236264,0.00013063,0.00007212818,0.0005282456,0.0001305981,0.0001561012,0.0001198411,0.0003234306],"category_scores_gemma":[0.000713763,0.000120045,0.00003495944,0.0002353595,0.000117335,0.000146873,0.00006338658,0.0002042032,0.00002026363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003044544,"about_ca_system_score_gemma":0.00003138032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001867248,"about_ca_topic_score_gemma":0.0002920598,"domain_scores_codex":[0.9979199,0.001186394,0.0002146246,0.0003397138,0.0001229546,0.0002164632],"domain_scores_gemma":[0.9973831,0.001072817,0.0001953131,0.0003656405,0.0008954753,0.00008772305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003326904,0.0007257484,0.1210156,0.00006540238,0.000213048,0.000003475598,0.03507954,0.000139196,0.004070232,0.4840076,0.009007086,0.3453404],"study_design_scores_gemma":[0.005123744,0.00003734499,0.07965365,0.0008895855,0.000119803,0.00006408488,0.003250469,0.6325104,0.0313186,0.00134147,0.2445537,0.001137125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1589939,0.00008453226,0.7980454,0.003017324,0.0001127257,0.0001998037,8.825156e-7,0.0001340185,0.03941149],"genre_scores_gemma":[0.974802,0.000026042,0.01113584,0.000365929,0.00007260602,0.00003815192,0.00008386681,0.00002806554,0.01344749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8158082,"threshold_uncertainty_score":0.4895293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0163398047893857,"score_gpt":0.274592864622581,"score_spread":0.2582530598331953,"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."}}