{"id":"W6945254384","doi":"10.25318/1210006601-eng","title":"Merchandise import and export price and volume indexes by sector and sub-sector, customs and balance of payments basis, for all countries, quarterly (2002)","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Balance of payments; Payment; Fishing; Price index; Weighting; Agriculture","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":[0.000679772,0.001448329,0.001195367,0.006788231,0.0008283443,0.00225979,0.001700491,0.00077396,0.05232495],"category_scores_gemma":[0.006917354,0.0007143561,0.0008424268,0.02396873,0.000335888,0.001078475,0.0008460275,0.001553737,0.0509896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006771999,"about_ca_system_score_gemma":0.01111524,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.707683,"about_ca_topic_score_gemma":0.7097925,"domain_scores_codex":[0.9986048,0.00007394578,0.0001860861,0.0002555165,0.0006083086,0.0002713743],"domain_scores_gemma":[0.9935393,0.0005229604,0.0006962693,0.000369513,0.004561638,0.0003102479],"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":[0.00003062816,0.00001307764,0.002450767,0.0002552294,0.00001732932,0.0000115922,0.00001685392,0.0002072602,0.00002891032,0.000439823,0.9949728,0.001555808],"study_design_scores_gemma":[0.0001380902,0.00001525398,0.05005694,0.0003425826,0.00003784758,0.00004298619,0.0002028427,0.0004868511,0.0002767427,0.0003641987,0.9479939,0.00004166706],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001675185,0.00003123407,0.00001622709,0.00002956705,0.000009795569,0.000006228141,0.9989386,0.00003507162,0.0007658393],"genre_scores_gemma":[0.0007437789,0.00009335329,0.0001000627,0.00002123589,0.0000072966,0.00003331602,0.996875,0.00002177775,0.002104055],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.292317,"threshold_uncertainty_score":0.5880771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006827531251026005,"score_gpt":0.2459697621907803,"score_spread":0.2391422309397543,"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."}}