{"id":"W6939224169","doi":"10.6068/dp14ba86f117369","title":"Trend 1998 - 2011. Statistics Canada. CANSIM: Construction - Nonresidential Building Construction | Country: Canada | Table: Capital expenditures on construction, by type of asset and North American Industry Classification System (NAICS) sector | Variable: Canals and waterways (x 1,000,000), Public administration | Units: $CAD, 1998-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-035.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Summary statistics; Asset (computer security); Stock (firearms); Capital (architecture); Publication; Index (typography)","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":[0.001422765,0.002283914,0.002306673,0.007898738,0.00283772,0.00443852,0.00451094,0.001322196,0.08619678],"category_scores_gemma":[0.01328991,0.00161987,0.001790269,0.03618179,0.0005544632,0.002370429,0.00200547,0.002696203,0.05178875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04395563,"about_ca_system_score_gemma":0.1050958,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9930205,"about_ca_topic_score_gemma":0.9921348,"domain_scores_codex":[0.9965796,0.0001717166,0.000321735,0.0004515386,0.001685351,0.0007901627],"domain_scores_gemma":[0.9729102,0.0008412787,0.0008942381,0.0007361149,0.02341923,0.001198979],"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":[0.00002191097,0.00000710215,0.001295762,0.0002077321,0.00001904993,0.000007231365,0.00001991726,0.0001302011,0.000008840202,0.0003962395,0.9962184,0.001667686],"study_design_scores_gemma":[0.0001246455,0.00001097893,0.02557465,0.0006419388,0.00005596554,0.00002509885,0.0004100093,0.0005114949,0.00018535,0.0005640626,0.9718257,0.00007014579],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006781254,0.00004474431,0.00002253078,0.00009778631,0.00001984964,0.0000110444,0.9987307,0.0000521713,0.0009534031],"genre_scores_gemma":[0.0008969005,0.0002575063,0.0002926147,0.0001062421,0.00001480979,0.00008687885,0.9939939,0.00009155003,0.004259598],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08619678,"threshold_uncertainty_score":0.3189219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02709671529475637,"score_gpt":0.2341565232743655,"score_spread":0.2070598079796091,"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."}}