{"id":"W2960397067","doi":"10.1163/24523666-00401006","title":"The Allen-Unger Global Commodity Prices Database","year":2019,"lang":"en","type":"article","venue":"Research Data Journal for the Humanities and Social Sciences","topic":"Historical Economic and Social Studies","field":"Economics, Econometrics and Finance","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Commodity; Database; Computer science; Range (aeronautics); Economics; Engineering; Finance","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":[0.00106471,0.001119658,0.001058177,0.01360154,0.0003910519,0.00251514,0.001391145,0.0008092776,0.07706811],"category_scores_gemma":[0.007148208,0.0006654286,0.0005709349,0.03109369,0.0002324445,0.003328027,0.001674509,0.001328268,0.09122765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009504786,"about_ca_system_score_gemma":0.002338995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009465452,"about_ca_topic_score_gemma":0.00697176,"domain_scores_codex":[0.9987207,0.0001208482,0.0002567578,0.0002427174,0.0005274932,0.0001313577],"domain_scores_gemma":[0.9961928,0.000745804,0.0006821291,0.0008169133,0.001327631,0.0002346416],"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":[0.0001472961,0.00003668075,0.004876199,0.0006659725,0.00005979001,0.0001242402,0.00008100441,0.000978659,0.0002622491,0.01015049,0.9348679,0.04774963],"study_design_scores_gemma":[0.00004529219,0.00001919512,0.01244294,0.0002437541,0.00003168739,0.0001252302,0.0001144119,0.0007782161,0.0004632191,0.005724891,0.9799615,0.00004969082],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002122747,0.0004646209,0.001506338,0.0002264925,0.00008576678,0.00006253982,0.9745977,0.001079956,0.01985391],"genre_scores_gemma":[0.006824942,0.0009352744,0.004300429,0.000105368,0.00005604279,0.0002536394,0.9789701,0.0004966222,0.008057531],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07706811,"threshold_uncertainty_score":0.2578185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5508550322956527,"score_gpt":0.4206310372582023,"score_spread":0.1302239950374504,"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."}}