{"id":"W4390926500","doi":"10.21437/speechprosody.2012-132","title":"Effects of temporal chunking on speech recall","year":2012,"lang":"en","type":"article","venue":"","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Association of Universities and Colleges of Canada; Université de Montréal","funders":"","keywords":"Chunking (psychology); Recall; Computer science; Speech recognition; Task (project management); Context (archaeology); Serial position effect; Natural language processing; Phone; Working memory; Cognitive psychology; Artificial intelligence; Free recall; Cognition; Psychology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006543804,0.0003638101,0.0003820449,0.0002063036,0.000128441,0.0005009426,0.0002502815,0.0002817352,0.001896924],"category_scores_gemma":[0.005812297,0.0001953462,0.0001751417,0.00009180241,0.0005980508,0.0003784912,0.0005500889,0.0004790207,0.0001546336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001700784,"about_ca_system_score_gemma":0.0001739354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006151224,"about_ca_topic_score_gemma":0.0005387574,"domain_scores_codex":[0.9995203,0.000163617,0.00006696703,0.00009891763,0.0001021832,0.00004794008],"domain_scores_gemma":[0.9923619,0.005804882,0.0008043026,0.0005078254,0.0002041393,0.0003169119],"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":[0.0103015,0.0002949338,0.006860916,0.0001564451,0.0001079181,0.0003290187,0.0009150034,0.0004386363,0.9608049,0.0001151476,0.00004819982,0.0196275],"study_design_scores_gemma":[0.0004160598,0.01327281,0.1984689,0.0000410547,0.0004812098,0.001073781,0.0006907361,0.002962694,0.7802607,0.001190503,0.001076399,0.0000651653],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989923,0.0002247947,0.000308884,0.00001839862,0.000006568601,0.000003867467,0.00002704368,0.00001135898,0.0004067541],"genre_scores_gemma":[0.9989625,0.0001436871,0.0004144485,0.00001834472,0.00001021165,0.000009166778,0.00004040023,0.00001853367,0.0003825922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001896924,"threshold_uncertainty_score":0.006345809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02628919771920388,"score_gpt":0.2884389373282955,"score_spread":0.2621497396090916,"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."}}