{"id":"W2004841825","doi":"10.3758/pbr.15.1.161","title":"There are many ways to be rich: Effects of three measures of semantic richness on visual word recognition","year":2008,"lang":"en","type":"article","venue":"Psychonomic Bulletin & Review","topic":"Reading and Literacy Development","field":"Psychology","cited_by":248,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia; University of Calgary","funders":"","keywords":"Lexical decision task; Categorization; Psychology; Referent; Word (group theory); Variance (accounting); Semantics (computer science); Natural language processing; Word recognition; Cognitive psychology; Artificial intelligence; Linguistics; Cognition; Computer science; Reading (process)","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":[0.00179972,0.000464079,0.000692886,0.0004213561,0.0003523689,0.001157762,0.0004180376,0.0005896711,0.003206049],"category_scores_gemma":[0.01342868,0.0003856018,0.0005472268,0.0002379627,0.001379496,0.002228378,0.001216392,0.001126649,0.0002862203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001472164,"about_ca_system_score_gemma":0.0002582796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000671769,"about_ca_topic_score_gemma":0.001577494,"domain_scores_codex":[0.9992919,0.0002226422,0.00007107168,0.0001544385,0.0002101137,0.00004977807],"domain_scores_gemma":[0.982761,0.01245833,0.001828357,0.001382288,0.0005484706,0.001021558],"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":[0.07175481,0.004028486,0.1932325,0.002492446,0.004567958,0.0003688598,0.007870274,0.001392072,0.4567188,0.002974338,0.00286143,0.2517381],"study_design_scores_gemma":[0.0006638687,0.01550927,0.906598,0.0001428854,0.001426347,0.0004966327,0.002048489,0.000972126,0.06197451,0.007893624,0.002128377,0.0001458487],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953722,0.0006585401,0.001103671,0.0002276974,0.00003881485,0.00002869251,0.00009742039,0.00003834845,0.002434666],"genre_scores_gemma":[0.9960704,0.0003417675,0.002114127,0.0001850934,0.00002718652,0.00002947516,0.0001258507,0.00004047257,0.001065758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003206049,"threshold_uncertainty_score":0.01072526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08083368561567807,"score_gpt":0.3134587542997477,"score_spread":0.2326250686840697,"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."}}