{"id":"W2184876889","doi":"10.19026/rjaset.7.629","title":"The Prediction for Shanghai Business Climate Index by Grey Model","year":2014,"lang":"en","type":"article","venue":"Research Journal of Applied Sciences Engineering and Technology","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Office for Philosophy and Social Sciences; National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Index (typography); Quarter (Canadian coin); Business model; Econometrics; Operations research; Computer science; Economics; Mathematics; Geography; Management","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":[0.0005582354,0.0004524437,0.0003692808,0.0009854054,0.0002385927,0.000620261,0.0003690172,0.0003816603,0.0009767984],"category_scores_gemma":[0.001843566,0.0001837995,0.0007737992,0.0009434389,0.0001775525,0.0007514066,0.0003926269,0.0004017313,0.0002168744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009162351,"about_ca_system_score_gemma":0.0007958811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04144807,"about_ca_topic_score_gemma":0.02304256,"domain_scores_codex":[0.9997622,0.00006061677,0.00001216101,0.00005114619,0.00006317856,0.00005067765],"domain_scores_gemma":[0.9996081,0.0001562355,0.00004425389,0.00003406987,0.0001111987,0.00004622667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001163606,0.00004843394,0.09590868,0.00006216723,0.0001079259,0.0002442537,0.0001474318,0.87308,0.001928229,0.004007578,0.003420755,0.02092818],"study_design_scores_gemma":[0.000004740776,0.00001649623,0.01348316,0.000004811517,0.00001553599,0.00001453974,0.00003417169,0.9841623,0.0003731764,0.001567692,0.000312549,0.00001074762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527663,0.0004662063,0.03952978,0.0007074852,0.00007119846,0.00002502802,0.001105837,0.000287494,0.005040733],"genre_scores_gemma":[0.9970785,0.0001262797,0.001723045,0.00001585731,0.000007319125,0.000008134715,0.0005169808,0.000008916038,0.0005148859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04144807,"threshold_uncertainty_score":0.08241361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06527438427851293,"score_gpt":0.3684855271278065,"score_spread":0.3032111428492935,"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."}}