{"id":"W7095157794","doi":"","title":"feature correlations 1 Further Evidence for Feature Correlations in Semantic Memory","year":2007,"lang":"en","type":"article","venue":"","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Semantic feature; Word (group theory); Meaning (existential); Domain (mathematical analysis); Semantic memory","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002518949,0.0006003662,0.0005289216,0.0007884875,0.0003914215,0.001412237,0.0008939072,0.001230494,0.0160739],"category_scores_gemma":[0.02543152,0.0005858517,0.0005640406,0.0008970953,0.001129937,0.00528762,0.001935063,0.001985621,0.001322069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005085919,"about_ca_system_score_gemma":0.0006018853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001356008,"about_ca_topic_score_gemma":0.00123364,"domain_scores_codex":[0.998353,0.0003045177,0.0001086529,0.0005585245,0.0005863044,0.00008907361],"domain_scores_gemma":[0.9623327,0.02230813,0.005635174,0.006034411,0.002775313,0.0009142816],"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":[0.007641593,0.001513728,0.1708705,0.001509514,0.0005797279,0.001074344,0.004784831,0.004238988,0.464895,0.03904042,0.007311296,0.29654],"study_design_scores_gemma":[0.0004437117,0.003685175,0.7938493,0.0001774275,0.0004787363,0.002908118,0.0006047424,0.02707792,0.05728767,0.1017087,0.0115655,0.0002130236],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9301423,0.0008713233,0.02845881,0.001153347,0.0001641613,0.00009330366,0.001085986,0.0005153843,0.0375153],"genre_scores_gemma":[0.9908885,0.0001449283,0.006022695,0.0001863989,0.00005788013,0.00003120896,0.000482545,0.00009944648,0.002086395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0160739,"threshold_uncertainty_score":0.05377257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06009102995015911,"score_gpt":0.3439838966419125,"score_spread":0.2838928666917534,"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."}}