{"id":"W2156526064","doi":"10.1037/a0026727","title":"The role of semantic diversity in lexical organization.","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":156,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Google Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Natural language processing; Redundancy (engineering); Artificial intelligence; Word lists by frequency; Word (group theory); Latent semantic analysis; Context (archaeology); Linguistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00112476,0.0001337155,0.0002129772,0.0001815808,0.0007208605,0.00002877832,0.0007032998,0.0001716823,0.0004340038],"category_scores_gemma":[0.0002475944,0.0001151028,0.0000954838,0.0004998664,0.0006241921,0.0003374542,0.0000442166,0.0002630043,0.00001772719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071343,"about_ca_system_score_gemma":0.0002964043,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04234119,"about_ca_topic_score_gemma":0.1842996,"domain_scores_codex":[0.9981224,0.0002630377,0.0004012631,0.0001654688,0.0001052838,0.0009425312],"domain_scores_gemma":[0.9984707,0.00006379873,0.0002554583,0.0002202256,0.0001166802,0.0008731892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007700588,0.000321685,0.7815536,0.000004040082,0.00007552672,0.000117004,0.1504256,0.000005059913,0.04110247,0.01669765,0.006652822,0.002967577],"study_design_scores_gemma":[0.002387851,0.0005621496,0.4802636,0.0001148125,0.00005859871,0.0006414297,0.4366801,0.000004310094,0.02284179,0.004288122,0.05152385,0.000633439],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780558,0.008677158,0.00001229711,0.00103403,0.001446347,0.0001644034,0.000008183777,0.000007596586,0.01059415],"genre_scores_gemma":[0.998823,0.00008953889,0.0001348733,0.0004438039,0.0003259006,0.0000036502,0.000004163523,0.00001262203,0.0001623783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.30129,"threshold_uncertainty_score":0.9640359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03261418555332898,"score_gpt":0.3128819868850811,"score_spread":0.2802678013317522,"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."}}