{"id":"W6901646809","doi":"10.6068/dp14ba7be99dd74","title":"Trend 2005 - 2011. Statistics Canada. CANSIM: Environment - Environmental Quality | Country: Canada | Table: Total capital expenditures of drinking water plants, by main source water type | Variable: All other source water combinations (x 1,000,000) | Units: $CAD, 2005-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-085.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Summary statistics; Official statistics; Water quality; Census; Environmental quality; Natural resource; Quality (philosophy)","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":[0.001965208,0.002140645,0.002339025,0.008211619,0.002655822,0.004719478,0.004224806,0.001336328,0.1015052],"category_scores_gemma":[0.01686587,0.00170856,0.002017825,0.03968263,0.0005842797,0.00255549,0.002109798,0.002848898,0.05345216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05414528,"about_ca_system_score_gemma":0.1384574,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936858,"about_ca_topic_score_gemma":0.9909209,"domain_scores_codex":[0.9953911,0.0002810542,0.0004953726,0.0005093585,0.002330515,0.0009925799],"domain_scores_gemma":[0.9667704,0.001123087,0.0009682548,0.0008410162,0.02882405,0.001473215],"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":[0.00001982547,0.000005801354,0.000816238,0.0002424303,0.00001805358,0.000006203299,0.00001488249,0.0001232272,0.000007848633,0.0004279189,0.9965913,0.001726218],"study_design_scores_gemma":[0.0001195359,0.00001051022,0.01956771,0.0007184866,0.0000582488,0.0000236369,0.0003410852,0.0005110658,0.0001660424,0.000649252,0.9777622,0.00007230145],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005420211,0.00006506369,0.00003138477,0.000161882,0.00003582914,0.00001662747,0.998154,0.00007086871,0.001410029],"genre_scores_gemma":[0.00116417,0.0004467515,0.0005054144,0.0002193052,0.00002414386,0.0001358572,0.9910254,0.0001505259,0.006328442],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1015052,"threshold_uncertainty_score":0.3928533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01853600336641807,"score_gpt":0.2287599941633625,"score_spread":0.2102239907969444,"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."}}