{"id":"W6957712376","doi":"10.6068/dp14ba8da65f346","title":"Trend 2001 - 2013. Statistics Canada. CANSIM: Construction - Nonresidential Engineering Construction | Country: Canada | Table: Public and private investment, summary by sector | Variable: Capital, machinery and equipment, Public, Agriculture, forestry, fishing and hunting (x 1,000,000) | Units: $CAD, 2001-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-036.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Agricultural Research and Practices","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Descriptive statistics; Census; Official statistics; Stock (firearms); Private sector; Public sector; Publication; Summary statistics; Statistical analysis","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":[0.001701992,0.002418605,0.002315442,0.007998619,0.00329472,0.004790016,0.004832542,0.001442664,0.1051076],"category_scores_gemma":[0.01585906,0.001690437,0.001904897,0.03627553,0.0006012537,0.002566336,0.002279019,0.002877836,0.05570924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05008072,"about_ca_system_score_gemma":0.1284412,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9948663,"about_ca_topic_score_gemma":0.9936951,"domain_scores_codex":[0.996095,0.0001981644,0.0003711435,0.0004983146,0.001923332,0.0009140266],"domain_scores_gemma":[0.9670091,0.001002755,0.0009374248,0.0008458942,0.02864303,0.001561774],"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":[0.00002154943,0.000006816426,0.001161225,0.0002326867,0.00001758438,0.000006844671,0.00002139908,0.0001209233,0.000009594485,0.0004010833,0.9960484,0.001951942],"study_design_scores_gemma":[0.0001201716,0.00001169102,0.02377306,0.000756759,0.00005825441,0.00002375583,0.000440927,0.0004743546,0.0001692995,0.0006268926,0.9734682,0.00007663548],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006672954,0.00005429674,0.0000306595,0.0001231591,0.00002817762,0.00001524998,0.9983749,0.00006428894,0.001242422],"genre_scores_gemma":[0.001085095,0.0003406808,0.0004504827,0.0001634628,0.00001963469,0.0001248096,0.9918181,0.0001303601,0.005867367],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1051076,"threshold_uncertainty_score":0.3633627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02975690405977846,"score_gpt":0.2302823940903644,"score_spread":0.200525490030586,"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."}}