{"id":"W4254741545","doi":"10.1002/div.3643","title":"Sherwin‐Williams Co.","year":2006,"lang":"en","type":"article","venue":"Mergent s Dividend Achievers","topic":"Polymer Science and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Automotive industry; Variety (cybernetics); Engineering; Advertising; Commerce; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005020481,0.0001101479,0.00008371562,0.00005305665,0.00009284598,0.00003890502,0.0002371228,0.00003527692,0.0005049729],"category_scores_gemma":[0.000001908254,0.0001070615,0.00006028431,0.0002378384,0.00003998488,0.0002187051,0.00002053203,0.00007492989,0.0006655425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002319561,"about_ca_system_score_gemma":0.000008057318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005574632,"about_ca_topic_score_gemma":0.00001495287,"domain_scores_codex":[0.9992705,0.000006310632,0.0001438757,0.0001475125,0.0001684304,0.0002633482],"domain_scores_gemma":[0.9996799,0.00001246615,0.00001456943,0.0002145425,0.00000472517,0.00007377902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003881861,0.0001426224,0.048772,0.00004262138,0.00007327315,0.0000111831,0.0001833651,0.03348871,0.1060712,0.01193849,0.7809337,0.01833892],"study_design_scores_gemma":[0.0002475844,0.00001386739,0.04182205,0.00001272806,0.00002580619,0.000003213343,0.00006168535,0.004998677,0.1133512,0.0008606811,0.8381532,0.0004492457],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8475308,0.001226075,0.006979944,0.0008676326,0.0008864732,0.0002152109,0.00005361253,0.001033043,0.1412072],"genre_scores_gemma":[0.9958006,0.00008138016,0.0002765579,0.00009499992,0.0001421909,0.00002827493,0.00003034667,0.00002030188,0.003525413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1482697,"threshold_uncertainty_score":0.8554425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005410277139403218,"score_gpt":0.2000039226514203,"score_spread":0.194593645512017,"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."}}