{"id":"W6957751418","doi":"10.6068/dp14ba881ec0346","title":"Trend 1992 - 1997. Statistics Canada. CANSIM: Construction - Machinery and Equipment | Country: Canada | Table: Capital expenditures on machinery and equipment, by type of asset and Standard Industrial Classification (1980 SIC) | Variable: Office furniture (x 1,000,000), Agricultural and related service industries | Units: $CAD, 1992-1997. 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; Official statistics; Census; Asset (computer security); Summary statistics; Capital (architecture); Investment (military); Capital expenditure; Publication","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":[0.001525411,0.002189239,0.00231801,0.00819128,0.00286497,0.004070944,0.004628852,0.001277667,0.08721112],"category_scores_gemma":[0.01305371,0.001596161,0.001630052,0.03677356,0.0005529069,0.002239353,0.001966812,0.002671237,0.04885091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04743105,"about_ca_system_score_gemma":0.113507,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9945609,"about_ca_topic_score_gemma":0.9933802,"domain_scores_codex":[0.99643,0.0001759498,0.0003320296,0.0004580767,0.001735131,0.0008689535],"domain_scores_gemma":[0.9743387,0.0007735366,0.0009292935,0.0006697993,0.02201575,0.001272945],"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":[0.00002362834,0.000007350197,0.0014003,0.0002361506,0.00002067359,0.000008487056,0.00002296991,0.0001283788,0.000009335388,0.0004225642,0.9959098,0.001810427],"study_design_scores_gemma":[0.0001290152,0.00001324892,0.03171372,0.0006840046,0.00006119989,0.00002865073,0.0005029137,0.0004710057,0.0001781585,0.000529376,0.9656197,0.00006903269],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008012616,0.00005355046,0.00002298301,0.0001049477,0.00002077582,0.0000122233,0.9986815,0.00004540896,0.0009785402],"genre_scores_gemma":[0.00111027,0.0002921999,0.0002879338,0.0001137986,0.00001490695,0.00009397289,0.9928358,0.00007940768,0.005171645],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08721112,"threshold_uncertainty_score":0.344138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03134704667300652,"score_gpt":0.2504336330211715,"score_spread":0.219086586348165,"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."}}