{"id":"W2080058093","doi":"10.1145/1101149.1101276","title":"The dancing genome project","year":2005,"lang":"en","type":"article","venue":"","topic":"Human Motion and Animation","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Choreography; Computer science; Movement (music); Process (computing); Motion capture; Convergence (economics); Genetic algorithm; Simple (philosophy); Rate of convergence; Vocabulary; Artificial intelligence; Motion (physics); Human–computer interaction; Computer vision; Dance; Computer graphics (images); Machine learning; Programming language; Key (lock)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009022634,0.001761928,0.001367143,0.00286582,0.002180511,0.002672853,0.001837809,0.0018793,0.03569898],"category_scores_gemma":[0.002199388,0.0004373125,0.0009688248,0.004704075,0.000344183,0.0006584765,0.001575412,0.001881186,0.01648221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008632714,"about_ca_system_score_gemma":0.002917594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01011927,"about_ca_topic_score_gemma":0.01386126,"domain_scores_codex":[0.9993615,0.00009733329,0.00004638827,0.0002350463,0.0001829005,0.00007699066],"domain_scores_gemma":[0.9995358,0.0001078036,0.00005387875,0.00007352878,0.0001027116,0.0001263122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002043497,0.0004921237,0.01292563,0.004298079,0.000770303,0.002395248,0.001132977,0.009571385,0.06193366,0.04617323,0.4315561,0.4267077],"study_design_scores_gemma":[0.0002505291,0.0001066446,0.01607244,0.0006220233,0.0002723073,0.0007747539,0.000264503,0.004069886,0.005824755,0.01683534,0.9547981,0.0001088047],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.05575601,0.02270667,0.1169257,0.004555813,0.003213442,0.001020413,0.654041,0.0156441,0.1261368],"genre_scores_gemma":[0.05470584,0.007707318,0.1241947,0.00103084,0.0001811652,0.0007787107,0.7834846,0.001647287,0.02626963],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03569898,"threshold_uncertainty_score":0.119425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01112043953047541,"score_gpt":0.2132991668025556,"score_spread":0.2021787272720802,"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."}}