{"id":"W1975471657","doi":"10.3758/bf03201255","title":"Turning an advantage into a disadvantage: Ambiguity effects in lexical decision versus reading tasks","year":2000,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Topic Modeling","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Lexical decision task; Categorization; Psychology; Ambiguity; Reading (process); Task (project management); Contrast (vision); Meaning (existential); Linguistics; Word (group theory); Cognitive psychology; Lexico; Disadvantage; Word recognition; Natural language processing; Artificial intelligence; Cognition; Computer science","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":[0.005987213,0.0008352992,0.001533224,0.0009822708,0.0005221484,0.003778428,0.0009975025,0.002580806,0.007392044],"category_scores_gemma":[0.08006375,0.0008433464,0.0005348412,0.0007382745,0.001065349,0.007589364,0.002387177,0.002778952,0.001345443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002565772,"about_ca_system_score_gemma":0.000384083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008482817,"about_ca_topic_score_gemma":0.0008011494,"domain_scores_codex":[0.9972451,0.0007288652,0.0003058454,0.0006575282,0.0008291225,0.000233537],"domain_scores_gemma":[0.9232948,0.06618809,0.003920127,0.003386867,0.001417807,0.001792217],"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":[0.03314973,0.004073207,0.09984984,0.001750408,0.0006249033,0.00133313,0.01447835,0.002888543,0.659192,0.01099148,0.004639799,0.1670287],"study_design_scores_gemma":[0.002805766,0.006289094,0.7656035,0.0002838291,0.001377456,0.003120646,0.006016341,0.02651174,0.06890072,0.1140863,0.00445115,0.0005534442],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919844,0.0003615626,0.001952325,0.0003716415,0.00008456722,0.0000269768,0.0001124884,0.00007655408,0.005029462],"genre_scores_gemma":[0.9960522,0.0001433609,0.001820869,0.000331023,0.0001464595,0.00004079918,0.0001822248,0.000287288,0.0009959105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007392044,"threshold_uncertainty_score":0.03166378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01855886472884696,"score_gpt":0.3081781869163606,"score_spread":0.2896193221875136,"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."}}