{"id":"W7834595","doi":"10.1177/070674379403900807","title":"あの商業キャラクターにも負けていない! データから見る｢くまモン｣のキャラクターパワー (飛ぶように売れた! キャラクタータイアップ販促総力特集(第3弾)「ゆるキャラから売るキャラへ」宣言! くまモン : 起用プロモーションの効果)","year":2013,"lang":"en","type":"article","venue":"Top promotions販促会議","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","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":[0.0002558334,0.0001635785,0.00007905545,0.001100971,0.0005642475,0.0003653351,0.0002571922,0.0001732627,0.002686111],"category_scores_gemma":[0.0008828431,0.0001828376,0.00009234589,0.0007172983,0.0003100389,0.0002632273,0.000273669,0.0002251609,0.0003798101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001294191,"about_ca_system_score_gemma":0.001274439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2949798,"about_ca_topic_score_gemma":0.5508466,"domain_scores_codex":[0.9998252,0.0000285499,0.000009275215,0.0000236916,0.0000486121,0.00006466149],"domain_scores_gemma":[0.9993922,0.00007738067,0.0002841556,0.0000214521,0.0001148997,0.000110001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001399052,0.00001500254,0.9944414,0.00001063365,0.000003859693,0.00008973452,0.0002863443,0.00002107443,0.0002234821,0.00001921921,0.0001765239,0.004698759],"study_design_scores_gemma":[7.315087e-7,0.00002073492,0.9989659,0.000007070333,0.000001544187,0.0001212849,0.0004709072,0.00003131683,0.00006221933,0.000004818151,0.0003118898,0.000001633276],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986093,0.0001596648,0.00006492344,0.00005659732,0.000003084695,0.0000105757,0.0002186519,0.000004466889,0.0008726523],"genre_scores_gemma":[0.9965282,0.0003735339,0.0003492354,0.0000497136,0.000007164697,0.000009745595,0.0004730302,0.000002116899,0.002207207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2949798,"threshold_uncertainty_score":0.5865257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03945600752928136,"score_gpt":0.3183718684596238,"score_spread":0.2789158609303425,"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."}}