{"id":"W6901560955","doi":"10.6068/dp1715302984b22","title":"TREND: Bureau of Labor Statistics. State and Metro Area Employment, Hours, and Earnings: All Employees | State: Alaska, Arizona, Arkansas, California, Colorado, Florida, Illinois, Indiana, Iowa, Kansas, Kentucky, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Nebraska, New Hampshire, New Jersey, New York, Ohio, Oregon, Pennsylvania, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, Washington DC, Wisconsin | Metropolitan Statistical Area: Akron, OH, Anchorage, AK, Baltimore City, MD, Boston-Cambridge-Newton, MA NECTA Division, Chicago-Naperville-Arlington Heights, IL Metropolitan Division, Denver-Aurora-Broomfield, CO, Des Moines-West Des Moines, IA, Detroit-Dearborn-Livonia, MI Metropolitan Division, Indianapolis-Carmel, IN, Lexington-Fayette, KY, Lincoln, NE, Little Rock-North Little Rock-Conway, AR, Los Angeles-Long Beach-Glendale, CA Metropolitan Division, McAllen-Edinburg-Mission, TX, Miami-Miami Beach-Kendall, FL Metropolitan Division, Milwaukee-Waukesha-West Allis, WI, Minneapolis-St. Paul-Bloomington, MN-WI, Minneapolis-St. Paul-Bloomington, MN-WI, Nashville-Davidson--Murfreesboro--Franklin, TN, Newark, NJ-PA Metropolitan Division, Phoenix-Mesa-Glendale, AZ, Portland-Vancouver-Hillsboro, OR-WA, Portland-Vancouver-Hillsboro, OR-WA, Provo-Orem, UT, Richmond, VA, Seattle-Bellevue-Everett, WA Metropolitan Division, Sioux Falls, SD, St. Louis, MO-IL, St. Louis, MO-IL, Washington-Arlington-Alexandria, DC-VA-MD-WV Metropolitan Division | Seasonally Adjusted: Non-Seasonally Adjusted | Industry: Private Service Providing, Manufacturing, Trade, Transportation, and Utilities, Retail Trade, Transportation and Utilities, Information, Financial Activities, Professional and Business Services, Education and Health Services, Leisure and Hospitality, Other Services, Government, 01/2008 - 01/2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 002-030-001","year":2020,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Nonfarm payrolls; Census; State (computer science); Service (business); Statistical analysis","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":[0.002733231,0.002063972,0.001701804,0.007317491,0.0009459087,0.003124293,0.002417125,0.001160696,0.1177105],"category_scores_gemma":[0.02156092,0.000947068,0.001153945,0.02470988,0.0003904376,0.003254175,0.001507532,0.004022864,0.1178952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002973958,"about_ca_system_score_gemma":0.008378001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07624859,"about_ca_topic_score_gemma":0.03808976,"domain_scores_codex":[0.9954876,0.000926573,0.0008009634,0.0008554441,0.001518471,0.0004108601],"domain_scores_gemma":[0.9788043,0.003381165,0.002172636,0.001221868,0.01374556,0.0006744473],"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":[0.0000508299,0.0000218707,0.001372704,0.0003488148,0.00002619048,0.0000125272,0.00003334463,0.00009242148,0.00001893244,0.0005437665,0.9863771,0.01110148],"study_design_scores_gemma":[0.0001717732,0.0000598586,0.01468769,0.001445862,0.00008232812,0.00005900166,0.0003347083,0.0004456477,0.00008384212,0.001712316,0.9808685,0.00004847789],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005099636,0.001052877,0.0009990403,0.0009554134,0.0009042697,0.0003596208,0.9801244,0.001306356,0.01378809],"genre_scores_gemma":[0.00600766,0.003622027,0.003237356,0.001253078,0.0005264892,0.003355904,0.9621984,0.001237217,0.01856179],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8822895,"threshold_uncertainty_score":0.3937809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01743292422849534,"score_gpt":0.2443118873330309,"score_spread":0.2268789631045356,"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."}}