{"id":"W2291556522","doi":"","title":"Towards a new model of semantic processing: Task-specific effects of concreteness and semantic neighbourhood density in visual word recognition","year":2015,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Concreteness; Natural language processing; Computer science; Semantic compression; Artificial intelligence; Semantic memory; Semantics (computer science); Word (group theory); Task (project management); Word recognition; Semantic computing; Cognitive psychology; Semantic similarity; Explicit semantic analysis; Word processing; Psychology; Cognition; Semantic technology; Linguistics; Semantic Web; 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":[0.002501249,0.0007646242,0.0008843084,0.0008218795,0.0003289043,0.001767233,0.001023491,0.0008037649,0.001766103],"category_scores_gemma":[0.005463452,0.0006319793,0.001045024,0.0003927336,0.003826364,0.004502747,0.001782248,0.001621402,0.000273706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006501754,"about_ca_system_score_gemma":0.0005223296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002049798,"about_ca_topic_score_gemma":0.001731818,"domain_scores_codex":[0.9992028,0.0001777326,0.00003721894,0.0003802605,0.0001597058,0.00004234224],"domain_scores_gemma":[0.9962928,0.002194544,0.0004954956,0.0007124601,0.0001486069,0.0001560885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004548359,0.0007979166,0.07372784,0.001014678,0.0007275735,0.0005769595,0.008057388,0.04992113,0.661301,0.08303474,0.0009507143,0.1153417],"study_design_scores_gemma":[0.0002613511,0.002969215,0.4009582,0.0001348504,0.0003743538,0.0009330142,0.001178127,0.1825569,0.04641608,0.3611853,0.002695219,0.0003375367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.811525,0.0006665218,0.1802685,0.0009559884,0.00005513458,0.00009896429,0.0002534203,0.000277835,0.005898465],"genre_scores_gemma":[0.9736745,0.0002323407,0.02509275,0.0001637134,0.00002222988,0.00008606473,0.0001442132,0.00008635792,0.0004977338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002501249,"threshold_uncertainty_score":0.013228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05114683958482304,"score_gpt":0.2579312648953866,"score_spread":0.2067844253105635,"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."}}