{"id":"W6938950353","doi":"10.6068/dp14ba84c2d4d79","title":"Trend 1997 - 2011. Statistics Canada. CANSIM: Government - Employment and Remuneration | Country: Canada | Table: Labour statistics by business sector industry and non-commercial activity, consistent with the System of National Accounts, by North American Industry Classification System (NAICS) | Variable: Hours worked for all jobs, Retail trade, Business sector | Units: Hours x 1,000, 1997-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-104.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Official statistics; Economic statistics; Government (linguistics); Remuneration; Census; Public sector; Wages and salaries; Social statistics; National accounts; Private sector","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":[],"consensus_categories":[],"category_scores_codex":[0.002100897,0.002394463,0.002716414,0.008492331,0.003376317,0.004648405,0.004913972,0.001439646,0.09255853],"category_scores_gemma":[0.0177048,0.001695529,0.00202169,0.03966749,0.0005854855,0.002507593,0.002217141,0.003209,0.06134809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04877891,"about_ca_system_score_gemma":0.127868,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936719,"about_ca_topic_score_gemma":0.9919165,"domain_scores_codex":[0.9957291,0.0002920933,0.0004313954,0.0005529309,0.002034316,0.0009601917],"domain_scores_gemma":[0.9657692,0.001135736,0.0009325497,0.0009339995,0.02982098,0.001407565],"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":[0.00001965382,0.000005694454,0.000788799,0.0002019985,0.00001809257,0.000006123303,0.00001754434,0.00008843651,0.000007308519,0.000318976,0.9970457,0.001481631],"study_design_scores_gemma":[0.0001324046,0.00001123434,0.02183798,0.0007945655,0.00006527406,0.00002701202,0.0004399952,0.0004348444,0.0001604836,0.0006410916,0.9753719,0.00008327875],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005135019,0.00005114751,0.00002343158,0.0001290379,0.00003052905,0.00001273931,0.9988036,0.00005594237,0.0008422504],"genre_scores_gemma":[0.0007269649,0.0002774573,0.0003471867,0.0001564089,0.00001984621,0.0001049655,0.9940193,0.0001107771,0.0042372],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09255853,"threshold_uncertainty_score":0.3539174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03324978982688067,"score_gpt":0.2382985367059274,"score_spread":0.2050487468790467,"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."}}