{"id":"W3164104738","doi":"10.1155/2021/5573650","title":"A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology of the People's Republic of China; Energy Foundation","keywords":"Coal; Index (typography); Computer science; China; Econometrics; Deep learning; Work (physics); Scale (ratio); Environmental science; Operations research; Environmental economics; Artificial intelligence; Economics; Engineering; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007307627,0.001077133,0.000902337,0.000799568,0.0003648884,0.0006303045,0.0009032114,0.0007190511,0.0009859167],"category_scores_gemma":[0.001205077,0.0003748594,0.0008067394,0.001048664,0.0001463576,0.001078567,0.0006189464,0.001027211,0.0002933989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004642077,"about_ca_system_score_gemma":0.000855725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01679105,"about_ca_topic_score_gemma":0.0139782,"domain_scores_codex":[0.9997199,0.00004370647,0.00002476675,0.00008788158,0.00006651305,0.00005720195],"domain_scores_gemma":[0.9996867,0.00009682228,0.00002920913,0.000026637,0.0001429027,0.00001771613],"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":[0.0001661189,0.0001586294,0.004594586,0.00006153367,0.0002184773,0.0001464344,0.00007339817,0.6792586,0.005098082,0.0009659492,0.003529139,0.305729],"study_design_scores_gemma":[0.000002056333,0.00001008307,0.0002630116,0.000002400114,0.00001116403,0.000005328654,0.000004657821,0.9990368,0.0003130634,0.0002290567,0.0001192603,0.000003047835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2160329,0.002831288,0.7720031,0.0005949637,0.0002808657,0.00007041767,0.0006567775,0.002913962,0.004615671],"genre_scores_gemma":[0.9330788,0.0007480577,0.06166765,0.0001835517,0.0001180764,0.00008726028,0.001000415,0.00005421639,0.003061956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01679105,"threshold_uncertainty_score":0.03338665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006929290727165627,"score_gpt":0.201418518093974,"score_spread":0.1944892273668084,"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."}}