{"id":"W6939227731","doi":"10.6068/dp14ba8d0648f89","title":"Trend 2001 - 2011. Statistics Canada. CANSIM: Construction - Residential Construction | Country: Canada | Table: Capital expenditures on construction, by type of asset and North American Industry Classification System (NAICS) sector | Variable: Optical fibre (aerial, underground and submarine) (x 1,000,000), Agriculture, forestry, fishing and hunting | Units: $CAD, 2001-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-037.","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; Stock (firearms); Summary statistics; Descriptive statistics; Asset (computer security); Index (typography); National accounts","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":[0.001479388,0.002374288,0.002301961,0.008865084,0.002788812,0.004410063,0.004353859,0.001345188,0.07515474],"category_scores_gemma":[0.01358444,0.001645746,0.001799649,0.03967382,0.0005422336,0.002281963,0.001994186,0.002714392,0.04389646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04913203,"about_ca_system_score_gemma":0.1096126,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9942926,"about_ca_topic_score_gemma":0.9935701,"domain_scores_codex":[0.9963709,0.0001739889,0.0003736114,0.0004557376,0.001808078,0.0008178311],"domain_scores_gemma":[0.9713947,0.000903414,0.001010232,0.0006984724,0.02476209,0.001231134],"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":[0.00002343382,0.000007547953,0.001589358,0.0002622931,0.00002178913,0.000008425515,0.00002236466,0.0001540801,0.000009033451,0.0004038617,0.9957236,0.001774264],"study_design_scores_gemma":[0.0001209525,0.00001281824,0.03530876,0.0007727895,0.00006674209,0.00002873705,0.0005077333,0.0005978929,0.0001967861,0.0005064933,0.9618014,0.00007886754],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007927293,0.00005278862,0.00002023098,0.00009455823,0.00002038405,0.00001044505,0.9988093,0.00004344042,0.0008695118],"genre_scores_gemma":[0.0009886478,0.0002914106,0.0002517239,0.0001013081,0.00001352529,0.00007841019,0.9938426,0.0000712703,0.004361037],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07515474,"threshold_uncertainty_score":0.3564795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0239257756421336,"score_gpt":0.2229375606887553,"score_spread":0.1990117850466217,"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."}}