{"id":"W1492561302","doi":"10.1016/s0079-7421(03)45002-0","title":"Semantic Memory: Some Insights from Feature-Based Connectionist Attractor Networks","year":2004,"lang":"en","type":"book-chapter","venue":"The Psychology of learning and motivation/The psychology of learning and motivation","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Categorization; Connectionism; Computer science; Feature (linguistics); Semantic memory; Artificial intelligence; Task (project management); Cognitive science; Natural language processing; Object (grammar); Descriptive knowledge; Artificial neural network; Psychology; Linguistics; Cognition","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.001150769,0.0009423624,0.001319602,0.0006199842,0.001322994,0.00007932718,0.0004956322,0.001657599,0.000483628],"category_scores_gemma":[0.0003590049,0.0007450535,0.0003305578,0.0002698242,0.002062451,0.0001339085,0.0001295866,0.004127805,0.00004573812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000614118,"about_ca_system_score_gemma":0.0001225413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001574134,"about_ca_topic_score_gemma":0.00004122247,"domain_scores_codex":[0.9949729,0.0009558453,0.001321965,0.001614525,0.000494394,0.0006403118],"domain_scores_gemma":[0.9939525,0.002351382,0.002416377,0.0007341799,0.0003722097,0.0001733669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009035574,0.002698285,0.6125907,0.0009065373,0.01079583,0.0002191897,0.02843992,0.026602,0.01360615,0.1369736,0.02304372,0.1350885],"study_design_scores_gemma":[0.005986147,0.001667521,0.8044298,0.00110274,0.0005836688,0.0001294656,0.0009058035,0.0002523699,0.00003738744,0.008921656,0.1747653,0.001218155],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8544631,0.01571118,0.014842,0.01267029,0.004416243,0.002611711,0.00005142288,0.0007428078,0.09449127],"genre_scores_gemma":[0.9503995,0.0006908975,0.0002302017,0.001220167,0.0006047256,0.00004964591,0.0004828739,0.0001783812,0.0461436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1918391,"threshold_uncertainty_score":0.9999772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02227163788801064,"score_gpt":0.2726168097459888,"score_spread":0.2503451718579782,"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."}}