{"id":"W2073248762","doi":"10.3758/brm.41.4.1210","title":"Grounding co-occurrence: Identifying features in a lexical co-occurrence model of semantic memory","year":2009,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Computer science; Co-occurrence; Feature vector; Word (group theory); Natural language processing; Artificial intelligence; Feature (linguistics); Semantics (computer science); Distributional semantics; Memory model; Vector space model; Space (punctuation); Semantic network; Vector space; Artificial neural network; Semantic similarity; Linguistics; Mathematics; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00156239,0.0003964552,0.0007986679,0.001242861,0.0005927992,0.002188247,0.001896993,0.00111484,0.00299294],"category_scores_gemma":[0.008469284,0.0004023424,0.001009169,0.001307141,0.001650742,0.005541423,0.001135756,0.00106973,0.0003343321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005819263,"about_ca_system_score_gemma":0.0007238287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002632489,"about_ca_topic_score_gemma":0.002411179,"domain_scores_codex":[0.9995433,0.0001328478,0.00003122619,0.0001399299,0.00007040824,0.00008225233],"domain_scores_gemma":[0.9950715,0.002886557,0.0006055367,0.0008527453,0.000312019,0.0002716492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00172883,0.0006941535,0.04562369,0.0002339197,0.000372189,0.001207472,0.001524816,0.1690396,0.02736326,0.591046,0.001883841,0.1592822],"study_design_scores_gemma":[0.00002647858,0.00007997197,0.004339358,0.00001091854,0.0000524508,0.0002228393,0.00008830092,0.678106,0.001363025,0.3154122,0.0002796473,0.00001887624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4284495,0.0001803127,0.5666202,0.0004993511,0.00004193547,0.00004073772,0.0001849754,0.0002413171,0.003741738],"genre_scores_gemma":[0.9804689,0.00006658478,0.0183065,0.00002675901,0.00002661874,0.00003394198,0.0001063453,0.00003373642,0.0009305032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00299294,"threshold_uncertainty_score":0.01001233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4630861836323995,"score_gpt":0.6144912133150822,"score_spread":0.1514050296826827,"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."}}