{"id":"W6957909487","doi":"10.6068/dp15884aaf66829","title":"TREND: Federal Reserve Board. Currency Exchange Rates: Exchange Rates | Convert From: United States | Convert To: Canada, 01/04/1971 - 11/10/2016. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 014-003-001","year":2016,"lang":"en","type":"other","venue":"Data Planet","topic":"Biodiesel Production and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Currency; Foreign-exchange reserves; Reserve requirement; Federal Reserve Economic Data; Open market operation; Reserve currency; Monetary reform; Exchange rate; Official cash rate","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":["insufficient_payload"],"category_scores_codex":[0.0003735787,0.0010626,0.001058383,0.0003122043,0.0002271804,0.0004218444,0.003023381,0.0005271155,0.07263638],"category_scores_gemma":[0.00003233823,0.0009475649,0.000003253195,0.0001681552,0.0002163483,0.0004056132,0.001138007,0.0006448276,0.00731034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001742135,"about_ca_system_score_gemma":0.000238694,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9709564,"about_ca_topic_score_gemma":0.8686711,"domain_scores_codex":[0.99532,0.0002912055,0.0009246925,0.001729972,0.0006747816,0.001059315],"domain_scores_gemma":[0.9935376,0.0005323175,0.0003177506,0.004829245,0.00001731523,0.0007657794],"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":[0.00004901041,0.00008652699,0.0000113413,0.0006977873,0.0003776391,0.00009070549,0.00001195048,0.000006189216,0.00001493725,0.00002443377,0.9978569,0.0007726274],"study_design_scores_gemma":[0.001055284,0.00004094633,0.000004174665,0.00009010496,0.0001754622,0.00001999111,0.0001082554,0.004239848,0.000002333731,6.859283e-7,0.9930375,0.001225469],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[9.533637e-7,0.00619718,0.00004551891,0.0002275241,0.001867655,0.001118872,0.9883126,0.0005479312,0.00168174],"genre_scores_gemma":[0.000006764663,0.005493938,0.0001192262,0.0007012997,0.001168328,0.0001494981,0.9803929,0.0003948675,0.0115732],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1022854,"threshold_uncertainty_score":0.9992975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03470485495051821,"score_gpt":0.2800020573545269,"score_spread":0.2452972024040087,"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."}}