{"id":"W4319602737","doi":"10.1177/09637214221128252","title":"Life Detection From Biological Motion","year":2023,"lang":"en","type":"article","venue":"Current Directions in Psychological Science","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Biological motion; Motion (physics); Motion detection; Observer (physics); Identification (biology); Psychology; Computer vision; Neurophysiology; Artificial intelligence; Point (geometry); Communication; Computer science; Neuroscience; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005569722,0.0001358197,0.0001308707,0.0004133095,0.0004882805,0.0001060947,0.0004025528,0.0000856245,0.0001206055],"category_scores_gemma":[0.004612041,0.0001018539,0.00006511105,0.005322988,0.0005842774,0.0003074077,0.0001268506,0.0003581127,0.0005739137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001045593,"about_ca_system_score_gemma":0.0000179209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001998228,"about_ca_topic_score_gemma":0.00001070958,"domain_scores_codex":[0.9977475,0.0001607267,0.0002661138,0.001014052,0.0003656596,0.0004458944],"domain_scores_gemma":[0.9991209,0.0002972774,0.00007008151,0.0002646495,0.00003339035,0.0002137118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003035423,0.0003230319,0.004936383,0.000001390418,4.536386e-7,0.000006447571,0.00003498667,0.0002226819,0.5686581,0.00175481,0.0002778637,0.4237535],"study_design_scores_gemma":[0.0005996414,0.0002840468,0.8735865,0.00003031981,0.000003950086,0.00001713675,0.00005871659,0.04262633,0.03216199,0.03707232,0.01309639,0.000462685],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989044,0.00003381995,0.00155252,0.0004670816,0.006370837,0.0002132114,0.00001442864,0.0005399905,0.001764177],"genre_scores_gemma":[0.9989243,0.0004973431,0.0000298355,0.0002823857,0.0001546367,0.00006794475,0.000005103089,0.000005167079,0.00003329156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8686501,"threshold_uncertainty_score":0.7376691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1480971155350113,"score_gpt":0.4019609694277145,"score_spread":0.2538638538927033,"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."}}