{"id":"W2546522479","doi":"10.3968/8829","title":"The Forecasting of Humanitarian Supplies Demand Based on Gray Relational Analysis and BP Neural Network","year":2016,"lang":"en","type":"article","venue":"Canadian social science","topic":"Safety and Risk Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gray (unit); Flood myth; Demand forecasting; Supply and demand; Grey relational analysis; Operations research; China; Flooding (psychology); Context (archaeology); Artificial neural network; Computer science; Artificial intelligence; Economy; Political science; History; Economics; Psychology; Archaeology; Law; Engineering; Microeconomics; Mathematical economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000777273,0.00006989719,0.00008699969,0.00022695,0.001895664,0.0001444055,0.0002154002,0.00002361813,0.00004908039],"category_scores_gemma":[0.0001017759,0.00004244295,0.0000463742,0.001179514,0.000523986,0.0003191112,0.00004570241,0.00003964165,0.000005059764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006794709,"about_ca_system_score_gemma":0.00006175759,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00649317,"about_ca_topic_score_gemma":0.09564603,"domain_scores_codex":[0.9991295,0.00000876478,0.0001275059,0.0001745654,0.0002453091,0.0003143336],"domain_scores_gemma":[0.9995811,0.0001094121,0.0001067237,0.00009282328,0.00008151508,0.00002842479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002055309,0.000006893831,0.4409821,0.00001154445,0.00004525619,0.000004664422,0.0001009946,0.001650763,0.00001599707,0.52223,0.002340539,0.03259072],"study_design_scores_gemma":[0.0003102702,0.0000114979,0.8738252,0.00002515038,0.000174223,1.474969e-7,0.0004218309,0.04974284,0.0000038685,0.008796235,0.06647171,0.0002170378],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6350328,0.0001346893,0.003332561,0.02536771,0.001155805,0.0007124061,0.00003182318,0.00006969609,0.3341625],"genre_scores_gemma":[0.9984725,0.000001906693,0.00004214179,0.000934973,0.000418806,0.000004110395,0.000002694206,0.000003807914,0.0001190541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5134337,"threshold_uncertainty_score":0.9994037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01879271644579614,"score_gpt":0.2061563607062587,"score_spread":0.1873636442604626,"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."}}