{"id":"W6957741651","doi":"10.6068/dp14ba85480da76","title":"Trend 1988 - 2005. Statistics Canada. CANSIM: Government - Revenue and Expenditures | Country: Canada | Table: Local general government revenue and expenditures, year ending December 31 | Variable: Resource conservation and industrial development, expenditures | Units: $CAD x 1,000, 1988-2005. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-107.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Revenue; Economic statistics; Government (linguistics); Official statistics; Government revenue; Census; Local government; Summary statistics; State (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002087242,0.002275219,0.00241615,0.009667025,0.003271451,0.004678532,0.004579393,0.001278438,0.08979598],"category_scores_gemma":[0.01508706,0.001746686,0.001683382,0.0425712,0.0005700016,0.002479341,0.001916006,0.002918674,0.05112318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0664623,"about_ca_system_score_gemma":0.1604001,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.995558,"about_ca_topic_score_gemma":0.9938673,"domain_scores_codex":[0.995334,0.0002491976,0.0004268344,0.000506926,0.002415419,0.001067729],"domain_scores_gemma":[0.9666092,0.0008930529,0.001055734,0.0008226369,0.02908275,0.00153667],"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":[0.00002273323,0.000006821594,0.0009675831,0.0002103305,0.00001741864,0.000006756628,0.00002040374,0.0001061132,0.000008425682,0.0005055132,0.9961839,0.001943944],"study_design_scores_gemma":[0.00009510617,0.00001126247,0.02296111,0.0006430408,0.00005223274,0.00002268247,0.000392185,0.0003926792,0.0001593288,0.0005190657,0.974684,0.00006730545],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007791179,0.00007358097,0.00002864387,0.0001642085,0.00003462268,0.00001886097,0.9979062,0.00006990939,0.001626016],"genre_scores_gemma":[0.001388755,0.0004556374,0.0004988478,0.000197372,0.00002326492,0.0001387951,0.9888071,0.0001369652,0.008353317],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08979598,"threshold_uncertainty_score":0.48222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02824711446245354,"score_gpt":0.2407158006669126,"score_spread":0.212468686204459,"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."}}