{"id":"W6975955977","doi":"10.6068/dp14ba854492854","title":"Trend 1992 - 1999. Statistics Canada. CANSIM: International Trade - Trade Patterns | Country: Canada | Table: Interprovincial and international trade flows at producer prices | Variable: Soft drinks and alcoholic beverages (x 1,000,000), International imports | Units: $CAD, 1992-1999. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-134.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"International comparisons; Official statistics; Economic statistics; Census; Summary statistics; Trade barrier; National accounts; Statistical analysis","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":[0.001619226,0.002389922,0.002492082,0.009021763,0.003303997,0.004928227,0.004573074,0.001413833,0.09728819],"category_scores_gemma":[0.0136504,0.001715551,0.001941113,0.04437587,0.0005952609,0.00262967,0.002200082,0.00285453,0.05899496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05014453,"about_ca_system_score_gemma":0.1288972,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994155,"about_ca_topic_score_gemma":0.9921171,"domain_scores_codex":[0.9963289,0.0002024803,0.0003761967,0.0004451043,0.001749476,0.0008978331],"domain_scores_gemma":[0.9725837,0.0007536598,0.0008624262,0.0007253276,0.02386967,0.001205283],"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":[0.00002275168,0.000006362186,0.001041666,0.0002327181,0.00001918109,0.000007737084,0.00001947948,0.0001050598,0.000008623879,0.0004071981,0.9965013,0.001627769],"study_design_scores_gemma":[0.0001226421,0.00001118521,0.02400403,0.0008089977,0.00005996328,0.00002739706,0.0004930171,0.0004344501,0.0001662498,0.0006195006,0.9731779,0.00007477205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005757973,0.00005058683,0.00002196224,0.0001052921,0.00002802717,0.00001422831,0.9985113,0.00005220961,0.001158783],"genre_scores_gemma":[0.0008968824,0.000332763,0.0003724458,0.0001254795,0.00001683204,0.000108504,0.9926984,0.0001026668,0.005346094],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09728819,"threshold_uncertainty_score":0.3638258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01946665078789494,"score_gpt":0.2490922987478776,"score_spread":0.2296256479599827,"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."}}