{"id":"W6977145747","doi":"10.6068/dp14ba8cc6b3469","title":"Trend 1994 - 2003. Statistics Canada. CANSIM: Construction - Machinery and Equipment | Country: Canada | Table: Capital and repair expenditures, industry sectors 31-33, manufacturing | Variable: Semiconductor and other electronic component manufacturing (x 1,000,000), Repair, machinery and equipment | Units: $CAD, 1994-2003. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-034.","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; Census; Official statistics; Capital (architecture); Investment (military); Publication; Capital expenditure; Descriptive statistics; Summary statistics","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":[0.001886588,0.002343787,0.002464574,0.008348235,0.003241391,0.004472007,0.004927287,0.001417141,0.08900277],"category_scores_gemma":[0.01680723,0.001619815,0.001923932,0.03704547,0.0005913639,0.002374371,0.002129406,0.002926857,0.05162034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04900023,"about_ca_system_score_gemma":0.1223055,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946443,"about_ca_topic_score_gemma":0.9927695,"domain_scores_codex":[0.9960616,0.0002203318,0.0003825242,0.0005341406,0.001860064,0.0009412052],"domain_scores_gemma":[0.9705744,0.0009928429,0.0009670646,0.0008906019,0.0251613,0.001413794],"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":[0.00002500695,0.000006499718,0.001122586,0.0002165208,0.00002031614,0.00000728953,0.00002058556,0.0001216234,0.000009143302,0.0003882418,0.9963214,0.001740727],"study_design_scores_gemma":[0.0001345175,0.00001175951,0.02423713,0.0006905999,0.00006186122,0.00002667859,0.0004080403,0.0004590452,0.0001746931,0.0006270597,0.9730939,0.00007465638],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005958875,0.00004697657,0.00002519393,0.000106315,0.00002274073,0.00001164353,0.9988066,0.00005298483,0.0008678968],"genre_scores_gemma":[0.0009134294,0.0002456943,0.0003584127,0.0001269505,0.00001519608,0.0001014944,0.9939341,0.0001005751,0.004204195],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08900277,"threshold_uncertainty_score":0.3555232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01711598131469633,"score_gpt":0.2332634871395856,"score_spread":0.2161475058248892,"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."}}