{"id":"W6920413050","doi":"10.6068/dp14ba8b5de5c78","title":"Trend 1992 - 2000. Statistics Canada. CANSIM: International Trade - Merchandise Imports | Country: Canada | Table: Merchandise imports and exports balance of payments and customs-based price and volume indexes for all countries | Variable: Balance of payments, Other iron and steel products, imports, Price index, Laspeyres fixed weighted | Units: 1992=100, 1992-2000. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-131.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Balance of payments; Official statistics; Economic statistics; Census; Price index; Balance (ability); Balance of trade; International comparisons; Payment; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001732894,0.002347264,0.002227237,0.008626889,0.003062068,0.004721144,0.004280438,0.001292155,0.1001043],"category_scores_gemma":[0.01471905,0.001521421,0.001665097,0.04029091,0.0005978916,0.002373059,0.002031374,0.002698457,0.06389136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04226564,"about_ca_system_score_gemma":0.1063221,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9923299,"about_ca_topic_score_gemma":0.9902081,"domain_scores_codex":[0.9965948,0.0001963009,0.0003455433,0.0004846132,0.001567891,0.0008107316],"domain_scores_gemma":[0.973443,0.0008816067,0.0008603261,0.0007955684,0.02284772,0.001171767],"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":[0.00001931008,0.000004999707,0.0008233225,0.0002027559,0.00001524596,0.000006554753,0.00001757096,0.00008437457,0.000008611813,0.0003482588,0.9969318,0.001537134],"study_design_scores_gemma":[0.0001010973,0.000008876597,0.01849116,0.0006809479,0.00004768461,0.0000242224,0.000372017,0.0003414351,0.0001483127,0.0005647162,0.9791559,0.00006369154],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005047301,0.00004215107,0.00001885246,0.00008588959,0.00002207126,0.00001040603,0.9987657,0.00004941448,0.0009550755],"genre_scores_gemma":[0.0006447653,0.0002254356,0.0002892788,0.0001156354,0.0000129256,0.00008473684,0.9943345,0.00009114322,0.004201697],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8998957,"threshold_uncertainty_score":0.3348821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01578494247135879,"score_gpt":0.2412575093472384,"score_spread":0.2254725668758796,"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."}}