{"id":"W2982456822","doi":"10.5430/ijhe.v8n7p1","title":"Methods of Studying the Semantic Function of Trademarks in the Industrial, Commercial and Advertising","year":2019,"lang":"en","type":"article","venue":"International Journal of Higher Education","topic":"Language, Communication, and Linguistic Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Trademark; Semiotics; Perception; Process (computing); Field (mathematics); Function (biology); Mechanism (biology); Computer science; Cognition; Advertising; Psychology; Linguistics; Business; Epistemology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002101001,0.00004104899,0.0001131955,0.0000962901,0.00007893882,0.00002579927,0.0003618218,0.00003507993,0.00005877516],"category_scores_gemma":[0.0004664143,0.00002510908,0.00003975618,0.0001367693,0.0001092002,0.00009873815,0.00002445201,0.0001501991,3.12485e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000486504,"about_ca_system_score_gemma":0.0001645817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112022,"about_ca_topic_score_gemma":0.0001203069,"domain_scores_codex":[0.9986078,0.000608171,0.0003492273,0.00004341012,0.0003400838,0.0000513383],"domain_scores_gemma":[0.9981417,0.0009807734,0.0004311673,0.00008745214,0.0003476071,0.00001127629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001981332,0.0005174921,0.4881697,0.00002428037,0.0003234498,6.562414e-7,0.2051759,0.00003859867,0.000815792,0.08470129,0.001971266,0.2180634],"study_design_scores_gemma":[0.0004002462,0.00005872507,0.9344763,0.0001708924,0.00006380223,0.000003386363,0.03641695,0.000006737299,0.00005294392,0.004478313,0.02382758,0.00004414578],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815697,0.002356041,0.0001240068,0.005669022,0.003345411,0.0001485344,7.02773e-7,0.000001584203,0.006785001],"genre_scores_gemma":[0.9984753,0.0002121979,0.0003637268,0.0001613448,0.0006273835,0.00000198257,7.498932e-7,0.000002469518,0.0001548269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4463066,"threshold_uncertainty_score":0.1693445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06919764230812565,"score_gpt":0.4355107532531277,"score_spread":0.366313110945002,"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."}}