{"id":"W4387373332","doi":"10.1075/cogls.21018.tor","title":"Metonymy in the nomenclature of Japanese traditional colors","year":2023,"lang":"en","type":"article","venue":"Cognitive Linguistic Studies","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Metonymy; Meaning (existential); Nomenclature; Mathematics; Linguistics; Chemistry; Communication; Art; Artificial intelligence; Computer science; Botany; Biology; Psychology; Philosophy; Epistemology; Taxonomy (biology)","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":[],"consensus_categories":[],"category_scores_codex":[0.0005733398,0.0001517234,0.0002943981,0.000199355,0.000109334,0.000008476258,0.0001389391,0.00007122964,0.0002482141],"category_scores_gemma":[0.003549096,0.0001042097,0.00009105317,0.0007371174,0.0003284346,0.00001649566,0.00003481017,0.0002083664,0.0002362853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001750447,"about_ca_system_score_gemma":0.00001970357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004836894,"about_ca_topic_score_gemma":0.00004604123,"domain_scores_codex":[0.9987236,0.0002400588,0.0002780174,0.0002609139,0.0002461207,0.0002513221],"domain_scores_gemma":[0.9965345,0.002851201,0.0001038383,0.000120601,0.0003665705,0.00002333236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005222906,0.0007780168,0.004796464,0.0002208308,0.00172994,0.001182376,0.8659811,0.000001327672,0.0001380181,0.1074953,0.0117766,0.005377703],"study_design_scores_gemma":[0.00229334,0.0004325553,0.329047,0.0003027981,0.0004783515,0.00003618239,0.5992575,0.00001139602,0.0001109635,0.06632236,0.001348293,0.0003592998],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8540826,0.004326174,0.00001204949,0.0002207991,0.001464781,0.0004220023,0.0002222726,0.0000619503,0.1391874],"genre_scores_gemma":[0.9973313,0.0001064209,0.00001704379,0.0005129589,0.0005247453,0.0002682722,0.0001049645,0.00001296867,0.001121328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3242505,"threshold_uncertainty_score":0.4249547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1113180407922029,"score_gpt":0.3766876473793302,"score_spread":0.2653696065871273,"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."}}