{"id":"W4240026813","doi":"10.1002/div.2825","title":"Sherwin‐Williams Co.","year":2005,"lang":"en","type":"article","venue":"Mergent s Dividend Achievers","topic":"Legal Cases and Commentary","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variety (cybernetics); Automotive industry; Business; Engineering; Commerce; Advertising; 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":[0.000118747,0.0001670428,0.000210246,0.00006432136,0.00009954344,0.00001970944,0.0001180008,0.00004798803,0.005259785],"category_scores_gemma":[0.00001799323,0.0001399899,0.0001930747,0.000119102,0.00004230686,0.0001704582,0.00005709077,0.0001899122,0.0009824318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001230337,"about_ca_system_score_gemma":0.00003063737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001225192,"about_ca_topic_score_gemma":0.00002235289,"domain_scores_codex":[0.9988284,0.00002547823,0.0002246644,0.0002475637,0.0003617085,0.0003122527],"domain_scores_gemma":[0.9993395,0.00002185135,0.00004478968,0.0003205209,0.00002086599,0.0002524857],"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":[0.0004516319,0.0008780972,0.2061852,0.00009286562,0.000432419,0.0002347412,0.0005246673,0.0001216848,0.01825931,0.0006959109,0.7120005,0.06012299],"study_design_scores_gemma":[0.00133156,0.0001898946,0.03666684,0.00003941203,0.0001440653,0.00005351842,0.0001250418,0.0001122117,0.0072717,0.00001298288,0.953841,0.000211729],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9592035,0.0009718116,0.00009862216,0.01992096,0.0004303872,0.0002572566,0.00001816918,0.0001185478,0.01898073],"genre_scores_gemma":[0.9823249,0.0002129606,0.0006479808,0.00932744,0.0005971956,0.00001458784,0.00007687967,0.00002734789,0.006770699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2418405,"threshold_uncertainty_score":0.9997954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897886073584875,"score_gpt":0.3040513047749375,"score_spread":0.2850724440390888,"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."}}