{"id":"W6939084572","doi":"10.6068/dp166c7d2c9dc16","title":"TREND: US Census Bureau, United States Census Bureau. County Business Patterns by NAICS Code (2003 - Current): Industrial Establishments (250 - 499 Employees) | State: Michigan | County: Kent, Ottawa | NAICS Code: 311, 3112, 3113, 3114, 3115, 3118, 3119, 312, 313, 314, 3149, 315, 3152, 3159, 316, 3162, 3169, 321, 3212, 3219, 322, 324, 325, 3252, 3253, 3254, 326, 327, 331, 332, 333, 334, 335, 336, 337, 339 | NAICS Description: Food Manufacturing, Grain and Oilseed Milling, Sugar and Confectionery Product Manufacturing, Fruit and Vegetable Preserving and Specialty Food Manufacturing, Dairy Product Manufacturing, Bakeries and Tortilla Manufacturing, Other Food Manufacturing, Beverage and Tobacco Product Manufacturing, Textile Mills, Textile Product Mills, Other Textile Product Mills, Apparel Manufacturing, Cut and Sew Apparel Manufacturing, Apparel Accessories and Other Apparel Manufacturing, Leather and Allied Product Manufacturing, Footwear Manufacturing, Other Leather and Allied Product Manufacturing, Wood Product Manufacturing, Veneer, Plywood, and Engineered Wood Product Manufacturing, Other Wood Product Manufacturing, Paper Manufacturing, Petroleum and Coal Products Manufacturing, Chemical Manufacturing, Resin, Synthetic Rubber, and Artificial Synthetic Fibers and Filaments Manu, Pesticide, Fertilizer, and Other Agricultural Chemical Manufacturing, Pharmaceutical and Medicine Manufacturing, Plastics and Rubber Products Manufacturing, Nonmetallic Mineral Product Manufacturing, Primary Metal Manufacturing, Fabricated Metal Product Manufacturing, Machinery Manufacturing, Computer and Electronic Product Manufacturing, Electrical Equipment, Appliance, and Component Manufacturing, Transportation Equipment Manufacturing, Furniture and Related Product Manufacturing, Miscellaneous Manufacturing, 2003 - 2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 001-007-011","year":2018,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Revenue; Agency (philosophy); Government (linguistics); Distribution (mathematics); Service (business); Internal revenue; Production (economics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006123739,0.001587753,0.001314106,0.004792899,0.0008626768,0.002186024,0.001597504,0.0007389446,0.07364495],"category_scores_gemma":[0.005091191,0.0007686222,0.0008948944,0.01916313,0.0002376996,0.002368616,0.0009653083,0.002163131,0.0867779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002503114,"about_ca_system_score_gemma":0.005752902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2040847,"about_ca_topic_score_gemma":0.121719,"domain_scores_codex":[0.9987979,0.0001185638,0.0001819936,0.0003079975,0.000388739,0.0002047749],"domain_scores_gemma":[0.9947102,0.0002574527,0.0004522341,0.0002138314,0.004145315,0.0002209771],"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":[0.00002874203,0.00001959362,0.002898107,0.0003413267,0.00002119001,0.00001224301,0.00003605529,0.00009922135,0.00002909906,0.0004132217,0.9918731,0.00422815],"study_design_scores_gemma":[0.0001606549,0.00004301518,0.07194528,0.0008284837,0.00007922009,0.00007965949,0.0005903579,0.0004447141,0.0001553144,0.0007058029,0.9249244,0.00004315452],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006998449,0.0002811923,0.0001835043,0.0002156307,0.0001953851,0.00009481017,0.9900978,0.0002986319,0.007933221],"genre_scores_gemma":[0.004314697,0.001171296,0.0007662553,0.0003762675,0.00009937252,0.0007008073,0.977202,0.0002827894,0.01508653],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9263551,"threshold_uncertainty_score":0.4057935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02457467589893365,"score_gpt":0.2393385512389966,"score_spread":0.2147638753400629,"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."}}