{"id":"W2288693139","doi":"10.3758/s13428-016-0720-6","title":"The Calgary semantic decision project: concrete/abstract decision data for 10,000 English words","year":2016,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":101,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Concreteness; Lexical decision task; Computer science; Lexicon; Natural language processing; Semantics (computer science); Semantic memory; Artificial intelligence; Variance (accounting); Word (group theory); Task (project management); Semantic property; Linguistics; Psychology; Cognitive psychology; Cognition","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002060117,0.0005606498,0.0005020511,0.00148704,0.0007124522,0.001129617,0.001001303,0.001053224,0.008131998],"category_scores_gemma":[0.01205936,0.0003379055,0.0003917832,0.001324503,0.0009877551,0.001276703,0.002014885,0.001414881,0.004192219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007709548,"about_ca_system_score_gemma":0.001592293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03831092,"about_ca_topic_score_gemma":0.05766708,"domain_scores_codex":[0.9987397,0.0003297904,0.0001262786,0.000233763,0.0004429799,0.0001274329],"domain_scores_gemma":[0.9910086,0.003285969,0.0006291773,0.002141894,0.001991844,0.0009425778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01824418,0.0046966,0.2928551,0.0006700203,0.0006293913,0.001806299,0.006285411,0.00615792,0.1112319,0.006191524,0.1282739,0.4229576],"study_design_scores_gemma":[0.0007998848,0.0007356165,0.8996921,0.0001040864,0.0001534927,0.0006295501,0.002985767,0.008316284,0.02589744,0.006738041,0.05374767,0.0002000828],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9334006,0.0002069791,0.004685785,0.0002345584,0.00008533761,0.0003099314,0.04996383,0.0004897324,0.01062317],"genre_scores_gemma":[0.8908808,0.0001303845,0.01792023,0.0001753525,0.00005113268,0.0008962292,0.07756211,0.0009669288,0.01141682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03831092,"threshold_uncertainty_score":0.07617587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4123654394581135,"score_gpt":0.5860038097873501,"score_spread":0.1736383703292366,"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."}}