{"id":"W6957615118","doi":"10.6068/dp14ba88929f626","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: Customs, Fresh fruits and berries, imports, Volume 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; Economic statistics; Official statistics; Census; Price index; Balance of trade; Balance (ability); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001504685,0.001865228,0.002514252,0.000457569,0.0003613652,0.000566263,0.001517097,0.001004233,0.00149738],"category_scores_gemma":[0.000265652,0.001858685,0.000001029572,0.0005392509,0.001393324,0.0008949401,0.001069481,0.001042071,0.000002436353],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007355979,"about_ca_system_score_gemma":0.02142547,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9983717,"about_ca_topic_score_gemma":0.9967132,"domain_scores_codex":[0.9902396,0.0004556837,0.002351641,0.002714207,0.002551653,0.001687255],"domain_scores_gemma":[0.9918633,0.0009642137,0.002774928,0.002551465,0.0001203255,0.001725813],"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":[0.0009544927,0.0001920363,0.007911468,0.001708187,0.001278223,0.001444127,0.00003571557,0.000009992717,0.000007239357,0.0001548402,0.9861922,0.0001114547],"study_design_scores_gemma":[0.003929083,0.0002187055,0.0002693491,0.0002392526,0.001405053,0.0005582561,0.0001950695,0.009868864,3.403755e-7,0.000002402847,0.9814016,0.001912028],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002757014,0.01514761,0.00002311387,0.00002727401,0.0007918931,0.002317104,0.9813341,0.0001337364,0.000197586],"genre_scores_gemma":[0.0006341939,0.003410009,0.000459346,0.0004759073,0.0002813347,0.0001282348,0.9929919,0.0006496467,0.0009694578],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02068988,"threshold_uncertainty_score":0.9994154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639541267468955,"score_gpt":0.2425351664057337,"score_spread":0.2261397537310441,"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."}}