{"id":"W4238300065","doi":"10.1002/div.3239","title":"Sherwin‐Williams Co.","year":2005,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00005949659,0.000110322,0.00008383317,0.00005272572,0.00008942764,0.00003450124,0.0002753956,0.00003545751,0.001019257],"category_scores_gemma":[0.000003413312,0.0001069584,0.00005986848,0.0002068393,0.0000360207,0.0003256895,0.00002446084,0.000089434,0.001558821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003105397,"about_ca_system_score_gemma":0.000009054928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006751961,"about_ca_topic_score_gemma":0.000008919856,"domain_scores_codex":[0.9992696,0.000006301659,0.0001393671,0.0001481479,0.0001685013,0.0002680749],"domain_scores_gemma":[0.9996093,0.00001244863,0.00001344486,0.0002337026,0.000004549202,0.0001265758],"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":[0.000005574237,0.0001645434,0.01711155,0.00003944696,0.0001496878,0.000005518811,0.001014029,0.05010999,0.07632952,0.005085417,0.6363857,0.213599],"study_design_scores_gemma":[0.0001327662,0.000007694895,0.004912319,0.000006761311,0.00001301806,0.000001814062,0.00005136871,0.006626355,0.06159408,0.00005945162,0.926367,0.0002273048],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.868274,0.00188151,0.006795059,0.003675747,0.0008929028,0.0002811551,0.00005356067,0.001325699,0.1168204],"genre_scores_gemma":[0.9955466,0.0002612235,0.0006558548,0.0003116624,0.0001904716,0.00002893365,0.00001366208,0.0000208116,0.002970766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2899814,"threshold_uncertainty_score":0.999894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009627201494745597,"score_gpt":0.2309389981214001,"score_spread":0.2213117966266545,"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."}}