{"id":"W2466426311","doi":"10.1075/ml.11.1.06tre","title":"What the Networks Tell us about Serial and Parallel Processing","year":2016,"lang":"en","type":"article","venue":"The Mental Lexicon","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Saint Mary's University","funders":"","keywords":"Computer science; Sentence; Speech recognition; Word (group theory); Frequency domain; Word lists by frequency; Magnetoencephalography; Task (project management); Amplitude; Natural language processing; Artificial intelligence; Mathematics; Psychology; Physics; Electroencephalography; Neuroscience","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":[0.001212094,0.0005511506,0.0007314654,0.001257369,0.0006225312,0.004750693,0.000996565,0.001880679,0.009761606],"category_scores_gemma":[0.007619951,0.0008931651,0.000716889,0.0009611693,0.003873689,0.020974,0.001020636,0.001441246,0.002329707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006177551,"about_ca_system_score_gemma":0.0003397292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001513572,"about_ca_topic_score_gemma":0.001600499,"domain_scores_codex":[0.9994028,0.0001535074,0.00002761322,0.0002630023,0.0000732388,0.00007978608],"domain_scores_gemma":[0.9972991,0.001390811,0.0003959393,0.0005406149,0.000183845,0.0001897974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.002061054,0.0001171268,0.08253823,0.001933489,0.001522286,0.001433781,0.009548486,0.01243825,0.02677155,0.5063407,0.01575991,0.3395351],"study_design_scores_gemma":[0.00005816208,0.0001024556,0.03822919,0.000330741,0.0002417511,0.000933378,0.001082972,0.008608506,0.002965084,0.9174123,0.02993841,0.0000969093],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5002327,0.03632677,0.2847589,0.04357723,0.001443156,0.00009644617,0.003706657,0.00158019,0.1282779],"genre_scores_gemma":[0.9450848,0.01026448,0.0298376,0.002659497,0.0007910833,0.000104651,0.0008092928,0.0003490102,0.01009951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009761606,"threshold_uncertainty_score":0.03265584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0233663772490335,"score_gpt":0.273607589988567,"score_spread":0.2502412127395335,"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."}}