{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009835988,0.0008515794,0.001124466,0.0001586438,0.0002513918,0.0004727961,0.001645637,0.0004068704,0.0009633535],"category_scores_gemma":[0.00004422682,0.000795415,6.530622e-7,0.0001280587,0.0004862759,0.0008922324,0.0005320293,0.0004926546,0.00000140105],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002113412,"about_ca_system_score_gemma":0.007018968,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.989835,"about_ca_topic_score_gemma":0.9422334,"domain_scores_codex":[0.9948292,0.0001627737,0.001287929,0.001557148,0.00128344,0.0008794614],"domain_scores_gemma":[0.9957756,0.0004094011,0.001513829,0.00161997,0.00007410911,0.0006070844],"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":[0.0001208178,0.00006090954,0.0003487823,0.0008479141,0.0004137849,0.0002719276,0.00002377566,0.00000141251,0.000003330437,0.0009413417,0.9965301,0.0004359558],"study_design_scores_gemma":[0.00119683,0.00008063392,0.000008762945,0.0001004165,0.0003837652,0.0008041813,0.0001366452,0.006828619,6.646258e-7,0.000006067862,0.9895718,0.0008816436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002398773,0.00779876,0.0009020988,0.00002293396,0.001780132,0.0007063039,0.9883919,0.00008322383,0.000312269],"genre_scores_gemma":[0.00007411022,0.002697314,0.001131003,0.0003528873,0.0002608467,0.00004538603,0.9941385,0.000159407,0.001140493],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04760151,"threshold_uncertainty_score":0.9999499,"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."}}