{"id":"W6957648455","doi":"10.6068/dp14ba839b0aa37","title":"Trend 1971 - 2011. Statistics Canada. CANSIM: International Trade - Merchandise Imports | Country: Canada | Table: Merchandise imports and exports, by major groups and principal trading areas for all countries | Variable: Balance of payments, Imports, Other countries (x 1,000,000) | Units: $CAD, 1971-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-131.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Principal (computer security); Official statistics; Economic statistics; Census; Statistical analysis; International comparisons; Summary statistics; Balance of trade; China","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":[0.001702112,0.00237728,0.002254432,0.00882246,0.003140678,0.00506948,0.004352138,0.001312472,0.1051354],"category_scores_gemma":[0.01529436,0.00163335,0.001767769,0.04303481,0.0006418594,0.002739374,0.002111492,0.002857955,0.06683772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04722529,"about_ca_system_score_gemma":0.1213009,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9933868,"about_ca_topic_score_gemma":0.9915015,"domain_scores_codex":[0.996137,0.0001890992,0.0003669755,0.0004897981,0.001940988,0.0008762314],"domain_scores_gemma":[0.9703746,0.0009510242,0.0008886132,0.0008394824,0.02568286,0.001263387],"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":[0.00001803162,0.000005320284,0.0008394712,0.0001913749,0.00001493772,0.000006409744,0.00001667636,0.00009833713,0.000008631006,0.0003839282,0.9968047,0.001612179],"study_design_scores_gemma":[0.00008711536,0.000008335417,0.01754636,0.0006054137,0.00004245498,0.00002076277,0.0003243381,0.0003260709,0.0001542741,0.0005221157,0.980301,0.0000618167],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005407671,0.00005058862,0.00002337629,0.0001079645,0.00002813385,0.00001308004,0.998319,0.00006211004,0.0013417],"genre_scores_gemma":[0.0007283132,0.0002964993,0.0003434423,0.0001304074,0.00001658728,0.00008903669,0.9927384,0.000116816,0.005540459],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1051354,"threshold_uncertainty_score":0.351713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01956113943827931,"score_gpt":0.2436499646357662,"score_spread":0.2240888251974869,"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."}}