{"id":"W2049460386","doi":"10.1186/1471-2202-15-s1-p66","title":"Auditory object feature maps with a hierarchical network of independent components?","year":2014,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Planum temporale; Auditory cortex; Computational auditory scene analysis; Computer science; Receptive field; Inferior colliculus; Natural sounds; Representation (politics); Perception; Object (grammar); Artificial intelligence; Feature (linguistics); Auditory system; Speech recognition; Pattern recognition (psychology); Psychology; Neuroscience; Nucleus; Linguistics","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":[0.0003206627,0.0001968802,0.0002253251,0.0000715823,0.0002786723,0.00007542298,0.0005743245,0.00006525632,0.000007019252],"category_scores_gemma":[0.0005577001,0.0001468312,0.00007273992,0.0006995157,0.0005050689,0.0002142125,0.000178351,0.0003704201,0.00001259757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002553725,"about_ca_system_score_gemma":0.00007454659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007191025,"about_ca_topic_score_gemma":0.00001752245,"domain_scores_codex":[0.9974968,0.0003038649,0.0002082483,0.0007408296,0.0007908182,0.0004594121],"domain_scores_gemma":[0.9987675,0.0003982153,0.0001927498,0.0004450367,0.00004050991,0.0001559701],"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":[0.0001765351,0.0001217006,0.007492817,0.00003411282,5.87818e-7,0.00001809869,0.00002490269,0.006125574,0.974802,0.009214453,0.00125878,0.0007305064],"study_design_scores_gemma":[0.002453176,0.003121122,0.6615257,0.0002095676,0.00003667716,0.00060938,0.00001714539,0.1802538,0.09468208,0.005854623,0.05005321,0.001183458],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834926,0.000006172338,0.01091229,0.0003982544,0.002474128,0.0003428274,0.00001523861,0.0001154484,0.002243003],"genre_scores_gemma":[0.9968668,0.000006915162,0.0005299738,0.001462995,0.0002781418,0.00001221741,0.000001729106,0.00001983171,0.0008213579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8801199,"threshold_uncertainty_score":0.5987602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02369327030532231,"score_gpt":0.2316789292150676,"score_spread":0.2079856589097452,"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."}}