{"id":"W2735516182","doi":"10.1017/s1365100516000511","title":"DISCERNING TRENDS IN COMMODITY PRICES","year":2017,"lang":"en","type":"article","venue":"Macroeconomic Dynamics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Obsolescence; Economics; Commodity; Nonparametric statistics; Competition (biology); Econometrics; Inflation (cosmology); Coal; Point (geometry); Microeconomics; Macroeconomics; Market economy; Mathematics; Business; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001778456,0.0002871061,0.0003897948,0.003428261,0.000218866,0.001508135,0.0004416411,0.000491972,0.001360864],"category_scores_gemma":[0.01570798,0.0003416947,0.0005182978,0.002483864,0.0002806187,0.001625775,0.0006360081,0.0008925675,0.0004896239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005054192,"about_ca_system_score_gemma":0.0005503495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005548738,"about_ca_topic_score_gemma":0.005541315,"domain_scores_codex":[0.9992486,0.0002253774,0.00008240086,0.0001788014,0.0002088308,0.00005599232],"domain_scores_gemma":[0.9927734,0.003979925,0.001604316,0.0007057172,0.0008262753,0.0001104276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002501677,0.0002113534,0.7672729,0.0002450449,0.0004214947,0.0003809494,0.0008638358,0.05282197,0.005682253,0.02538413,0.003454555,0.1430114],"study_design_scores_gemma":[0.00002485222,0.0002151415,0.6223662,0.00009105729,0.00008875561,0.0004013307,0.0007919291,0.3134791,0.008700676,0.0395598,0.01414464,0.0001365086],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8567642,0.0004513234,0.1269316,0.0006018498,0.00005182772,0.00009719043,0.006376847,0.0006441339,0.00808104],"genre_scores_gemma":[0.9701666,0.0002284175,0.02409775,0.00006925636,0.00002932532,0.00003947975,0.004535775,0.00004239111,0.0007907951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005548738,"threshold_uncertainty_score":0.01103288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02981993395799766,"score_gpt":0.2589803601066068,"score_spread":0.2291604261486091,"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."}}