{"id":"W2507290456","doi":"10.1017/s0140525x15000825","title":"How long is now? The multiple timescales of language processing","year":2016,"lang":"en","type":"letter","venue":"Behavioral and Brain Sciences","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bottleneck; Hierarchy; Computer science; Process (computing); Function (biology); Cognitive science; Window (computing); Hierarchical organization; Natural language processing; Artificial intelligence; Psychology; Programming language; Biology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003319989,0.0002473037,0.0002781729,0.000107387,0.0004109302,0.000235066,0.0008010946,0.0002499806,0.00004489035],"category_scores_gemma":[0.0001599252,0.0001157159,0.0001003255,0.0002409514,0.002818425,0.0002479855,0.0001932497,0.0004306675,0.00000830984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006143821,"about_ca_system_score_gemma":0.00007406253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003750455,"about_ca_topic_score_gemma":0.0000275442,"domain_scores_codex":[0.998086,0.0001752582,0.0002044679,0.0006879274,0.0004196004,0.0004267724],"domain_scores_gemma":[0.9988561,0.0005671827,0.0002582025,0.0002488557,0.0000332682,0.00003639077],"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":[0.000009944112,0.00004746944,0.002408948,0.0001153218,0.000002665876,0.001382139,0.00355939,3.532015e-8,0.5087187,0.000004189669,0.3918234,0.09192778],"study_design_scores_gemma":[0.0006472014,0.0006471984,0.0006648166,0.0005219239,0.00009919031,0.001268215,0.001386075,0.00003895606,0.5154365,0.0002276974,0.4780032,0.001059044],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.4716821,0.0005961373,0.000005668547,0.5270992,0.0001703003,0.0001863325,0.0001013821,0.00003558298,0.0001232934],"genre_scores_gemma":[0.5789364,0.00004455574,0.00007247124,0.3992617,0.001258773,0.00001780261,0.000007425236,0.00002295793,0.02037796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1278376,"threshold_uncertainty_score":0.9998953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07000899875789122,"score_gpt":0.3310532787131568,"score_spread":0.2610442799552655,"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."}}