{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001416172,0.0001180346,0.00009358019,0.0003241517,0.0003606574,0.0001316252,0.0002656435,0.0000325337,0.00003056223],"category_scores_gemma":[0.008973585,0.0001083058,0.00001230202,0.002519406,0.002258805,0.001711424,0.0001453291,0.0001696644,0.00006156521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006404587,"about_ca_system_score_gemma":0.0001946484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002790072,"about_ca_topic_score_gemma":0.0000544405,"domain_scores_codex":[0.9978466,0.00004568143,0.0001843433,0.0009015227,0.0005096561,0.0005121874],"domain_scores_gemma":[0.9993118,0.0001890601,0.00007196046,0.0001451409,0.0001499338,0.0001321111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003597247,0.00003415906,0.0009261652,0.000003179494,5.716326e-8,0.00002607935,0.0005781662,0.000003929192,0.97496,0.0002188452,0.000007099398,0.02320635],"study_design_scores_gemma":[0.000319476,0.00009064974,0.11663,0.00005008124,0.00000232938,0.0001565594,0.0005872559,0.0002708407,0.8809685,0.0005917799,0.0001406387,0.000191849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941142,0.000003344827,0.0005250534,0.0002220735,0.001004364,0.0003458458,0.000003704122,0.00004737307,0.003734106],"genre_scores_gemma":[0.9983743,0.00004076745,0.000105689,0.001146602,0.0001315502,0.000004403788,3.89259e-7,0.000005746876,0.0001905333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1157039,"threshold_uncertainty_score":0.9993743,"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."}}