{"meta":{"query_hash":"72af162bb53c","filters":{"venue":"Journal of Chinese Economic and Business Studies"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/72af162bb53c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Chinese+Economic+and+Business+Studies"},"results":[{"id":"W4412845518","doi":"10.1080/14765284.2025.2538934","title":"Price predictions of scrap steel for north China via machine learning","year":2025,"lang":"en","type":"article","venue":"Journal of Chinese Economic and Business Studies","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Scrap; China; Economics; Materials science; Metallurgy; Geography","score_opus":0.04001446240897147,"score_gpt":0.34715090675193605,"score_spread":0.3071364443429646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412845518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9531068,0.0046979073,0.0381894,0.0020221944,0.00078134984,0.00025214933,0.000047190468,0.000009666672,0.00089330436],"genre_scores_gemma":[0.99759746,0.0005079276,0.0006478142,0.00002920408,0.00017202013,0.000016603075,0.0000015455321,0.0000071882255,0.0010202547],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982658,0.000077453995,0.0011729383,0.00019848118,0.0001649886,0.00012032569],"domain_scores_gemma":[0.99636275,0.0013280223,0.0011789043,0.00020689415,0.000880216,0.00004323183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017819991,0.00014775468,0.0006921347,0.0004516652,0.00032418303,0.00005597028,0.000342952,0.0000358383,0.000015684107],"category_scores_gemma":[0.0017688188,0.00009207317,0.00013873872,0.0005171628,0.00016580246,0.00036062568,0.00014302589,0.000116608826,0.0000037981722],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002561199,0.00013223887,0.959593,0.00021542043,0.0007514745,0.0000011694037,0.0018560453,0.019844526,0.00024985595,0.0047505363,0.0028960297,0.009453604],"study_design_scores_gemma":[0.001027084,0.00008432737,0.91553676,0.00008992118,0.000091368915,0.000040158786,0.00066842703,0.006401116,0.000016872356,0.065959595,0.009977551,0.00010681677],"about_ca_topic_score_codex":0.000015383584,"about_ca_topic_score_gemma":0.00012835441,"teacher_disagreement_score":0.061209057,"about_ca_system_score_codex":0.00005212153,"about_ca_system_score_gemma":0.00010979321,"threshold_uncertainty_score":0.3754635},"labels":[],"label_agreement":null}]}