{"id":"W4251564233","doi":"10.1002/div.1269","title":"Sherwin‐Williams Co.","year":2004,"lang":"en","type":"article","venue":"Mergent s Dividend Achievers","topic":"Corporate Governance and Law","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variety (cybernetics); Automotive industry; Business; Engineering; 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":[0.0001867855,0.0002327003,0.0002007604,0.0001102973,0.0002240671,0.0001971955,0.0003530731,0.00006559477,0.001628938],"category_scores_gemma":[0.00003863599,0.0002144462,0.0001575441,0.0004172874,0.00006470428,0.001324151,0.0001345721,0.0001488126,0.003979501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007408636,"about_ca_system_score_gemma":0.00002909669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001335111,"about_ca_topic_score_gemma":0.000281948,"domain_scores_codex":[0.998558,0.000004632078,0.0002581381,0.0003464134,0.0004280846,0.0004047517],"domain_scores_gemma":[0.9993968,0.000009021799,0.0002012591,0.0003117234,0.00005397185,0.00002723587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002648002,0.0009418978,0.332882,0.0004275126,0.0003635072,0.0003451814,0.0002908875,0.005723269,0.01089233,0.2928869,0.3326159,0.02236579],"study_design_scores_gemma":[0.001154869,0.00001517252,0.04798989,0.00006254228,0.00006664256,0.000001957224,0.0001458077,0.00004515709,0.0007886861,0.005650488,0.9436044,0.0004743795],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9384985,0.0002098222,0.0004477394,0.003486078,0.001522551,0.000249914,0.000007712764,0.0002598172,0.05531787],"genre_scores_gemma":[0.9919889,0.00006082916,0.00008320249,0.004351499,0.001187921,0.00001604795,0.00005319429,0.0000386763,0.002219759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6109885,"threshold_uncertainty_score":0.9992837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001145132762391,"score_gpt":0.2214480532136896,"score_spread":0.2014366018860657,"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."}}