{"id":"W2984485448","doi":"10.1123/jmld.2019-0010","title":"Automated Measures of Force and Motion Can Improve Our Understanding of Infants’ Motor Persistence","year":2019,"lang":"en","type":"article","venue":"Journal of Motor Learning and Development","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Persistence (discontinuity); Motion (physics); Psychology; Cognition; Cognitive psychology; Field (mathematics); Motor skill; Developmental psychology; Human–computer interaction; Computer science; Artificial intelligence; Neuroscience; Engineering","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":[0.0008578873,0.0001495227,0.0003905739,0.0002389073,0.0000950274,0.00002143139,0.00008707921,0.00009090201,0.00003635165],"category_scores_gemma":[0.0001275226,0.0001248631,0.00006028631,0.00008981803,0.00003845499,0.00006750589,0.00004875832,0.0003069707,0.000002875657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001103596,"about_ca_system_score_gemma":0.0001480287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001257571,"about_ca_topic_score_gemma":0.000002236854,"domain_scores_codex":[0.9985846,0.0001061521,0.0005883714,0.0001885541,0.0003344813,0.0001978708],"domain_scores_gemma":[0.998862,0.00007816152,0.0007230184,0.00006849276,0.0001620925,0.0001062602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001266865,0.0002140305,0.7844868,0.0004568239,0.0009039877,0.00002850591,0.0452601,0.0001662923,0.1189285,0.0005366205,0.000137439,0.047614],"study_design_scores_gemma":[0.002391678,0.001339478,0.9633467,0.0006907069,0.00004893913,0.0001398367,0.0278133,0.0004251248,0.002004352,0.00004828472,0.001455303,0.0002963322],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975902,0.000539265,0.0005702256,0.0001343044,0.0003355664,0.0001357002,0.000001733881,0.000024574,0.0006684452],"genre_scores_gemma":[0.99582,0.00006517399,0.002324107,0.00001919376,0.0000338767,9.864555e-7,0.000001239149,0.00001489846,0.001720531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1788598,"threshold_uncertainty_score":0.509177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04217330429766215,"score_gpt":0.273218061662619,"score_spread":0.2310447573649569,"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."}}