{"id":"W4411580073","doi":"10.1016/j.engappai.2025.111442","title":"A decomposition–integration interval prediction strategy for iron ore shipping freight rates with reinforcement learning","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; Key Technologies Research and Development Program; National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Interval (graph theory); Decomposition; Iron ore; Artificial intelligence; Reinforcement; Operations research; Composite material; Mathematics","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":[0.001189402,0.000625133,0.0009781407,0.0004414285,0.0003097692,0.0006440068,0.001121016,0.0009462347,0.002302872],"category_scores_gemma":[0.002737754,0.00036027,0.0004544686,0.0003409433,0.0004599224,0.0008292213,0.0008139507,0.001069313,0.0002813828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007304007,"about_ca_system_score_gemma":0.0009398718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009822438,"about_ca_topic_score_gemma":0.005925924,"domain_scores_codex":[0.9996691,0.00008127219,0.00001991395,0.0000945632,0.0000743372,0.00006080531],"domain_scores_gemma":[0.9990166,0.0005176741,0.00008896009,0.0000558871,0.0002516406,0.00006921119],"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":[0.0001980911,0.0001540428,0.0009414362,0.00004803355,0.00004988654,0.00007686581,0.00007144158,0.8787332,0.003596051,0.006479587,0.001353665,0.1082976],"study_design_scores_gemma":[0.000005107303,0.00001217675,0.00005674982,0.000001690109,0.000003265923,0.000003252328,0.000001465773,0.9992254,0.0001685499,0.0004728929,0.00004746285,0.000001995886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04060871,0.0001657132,0.9561451,0.0001519041,0.00006683706,0.00004030912,0.00002443599,0.0002943825,0.002502693],"genre_scores_gemma":[0.9192191,0.00007584384,0.0783141,0.00009549381,0.00004057407,0.00007223487,0.0000559943,0.00003463329,0.00209198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009822438,"threshold_uncertainty_score":0.01953053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174399316271588,"score_gpt":0.26740276989058,"score_spread":0.2499628382634212,"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."}}