{"id":"W6957954911","doi":"10.6068/dp14baa3ca09b32","title":"Trend 1998 - 2003. Statistics Canada. CANSIM: Manufacturing - General | Country: Canada | Province: Northwest Territories | Table: Annual survey of manufactures (ASM), sales of manufactured goods by North American Industry Classification System (NAICS) and industry sub-sector | Variable: Manufacturing, Incorporated businesses with employees having sales of manufactured goods greater than or equal to $30,000 | Units: $CAD x 1,000, 1998-2003. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-149.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Economic statistics; Official statistics; Manufacturing; Summary statistics; Statistical analysis; Publication; Statistical survey; Annual report","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":[0.001663123,0.002322063,0.002717474,0.008325009,0.003116355,0.004412725,0.005170649,0.001423478,0.08177884],"category_scores_gemma":[0.01490631,0.001605994,0.001840551,0.04142408,0.000616074,0.00227511,0.001979915,0.002731046,0.05836518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04643598,"about_ca_system_score_gemma":0.1131746,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9941449,"about_ca_topic_score_gemma":0.9929832,"domain_scores_codex":[0.996516,0.0002082689,0.000365562,0.0005353185,0.001536462,0.0008384365],"domain_scores_gemma":[0.9729605,0.0009686293,0.0009431555,0.0008873745,0.02298092,0.001259402],"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":[0.00002221479,0.000005092118,0.0009649551,0.0001799696,0.00001771114,0.000006081115,0.00001629052,0.00009157945,0.000007842186,0.0002595242,0.9972383,0.001190451],"study_design_scores_gemma":[0.0001559783,0.0000115945,0.02405728,0.0006405808,0.00006384358,0.00002770546,0.0004441303,0.0004777304,0.0001722804,0.0005533869,0.9733232,0.00007238504],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005202886,0.00003841349,0.00001655215,0.00008740375,0.00002124965,0.000008970018,0.9990547,0.00004898102,0.0006716408],"genre_scores_gemma":[0.0006819469,0.0001743684,0.0002158216,0.0001052322,0.00001301707,0.00007009119,0.9957742,0.00006758298,0.002897723],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08177884,"threshold_uncertainty_score":0.3369182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02943794027516623,"score_gpt":0.2461679532185629,"score_spread":0.2167300129433966,"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."}}