{"id":"W2101238311","doi":"10.1080/03640210709336985","title":"Speed, Accuracy, and Serial Order in Sequence Production","year":2007,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Production (economics); Sequence (biology); Speech recognition; Event (particle physics); Context (archaeology); Artificial intelligence; Natural language processing","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":[0.004224695,0.0009619298,0.0006601769,0.001407127,0.0002662349,0.002341093,0.0009379882,0.001093582,0.002670803],"category_scores_gemma":[0.04991869,0.0007789243,0.00103135,0.000748325,0.001785864,0.003523847,0.00115324,0.0008394687,0.0007765139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001025028,"about_ca_system_score_gemma":0.0006445668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003243089,"about_ca_topic_score_gemma":0.001693575,"domain_scores_codex":[0.9986646,0.0003042789,0.0001157579,0.0004232446,0.0003585239,0.0001335856],"domain_scores_gemma":[0.9662082,0.02327638,0.005706511,0.002970197,0.001272391,0.0005662823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002987772,0.000559388,0.2061491,0.0003969588,0.0004652659,0.0006898856,0.001557758,0.5772471,0.03835277,0.04288825,0.0008768167,0.1278288],"study_design_scores_gemma":[0.0002153413,0.0006225174,0.1413576,0.00007045847,0.0001364228,0.000848749,0.00009472068,0.7565749,0.009610627,0.08961157,0.0007066873,0.0001503756],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8676051,0.0005722383,0.1263437,0.000232159,0.00003718492,0.00008125722,0.0002064111,0.0002332682,0.004688413],"genre_scores_gemma":[0.9862764,0.0002044818,0.01189153,0.00002279588,0.00001924797,0.00005482537,0.0001392223,0.00006402539,0.00132744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004224695,"threshold_uncertainty_score":0.02234262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09196995956501455,"score_gpt":0.3653810995393409,"score_spread":0.2734111399743264,"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."}}