{"id":"W4322632036","doi":"10.1016/j.apenergy.2023.120888","title":"Development of an integrated BLSVM-MFA method for analyzing renewable power-generation potential under climate change: A case study of Xiamen","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Renewable energy; Climate change; Electricity generation; Environmental science; Photovoltaic system; Wind power; Fossil fuel; Environmental economics; Environmental engineering; Engineering; Power (physics); Waste management; Economics; Electrical engineering; Ecology","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":[0.000555616,0.0006641038,0.0006088031,0.0007688968,0.0004114084,0.0005808594,0.0006802201,0.000722247,0.002030174],"category_scores_gemma":[0.0007292756,0.000298015,0.0005387877,0.0004727911,0.0001352695,0.0006077112,0.0004295042,0.0004121444,0.0004365978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000257028,"about_ca_system_score_gemma":0.0006847265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01252285,"about_ca_topic_score_gemma":0.01709313,"domain_scores_codex":[0.9998617,0.0000330369,0.000009592698,0.00003432032,0.00004302771,0.00001838303],"domain_scores_gemma":[0.9997514,0.00008846314,0.00001926346,0.00001764418,0.0001098206,0.00001347656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002223676,0.0003055177,0.008471002,0.0002361534,0.0001797082,0.0003601645,0.0001574365,0.4473485,0.03161368,0.001596229,0.002458123,0.5070511],"study_design_scores_gemma":[0.000004487533,0.00001779089,0.00108022,0.000002536925,0.000008448434,0.00001192186,0.00001411945,0.9974719,0.0009291697,0.0001549722,0.0003002003,0.000004242581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1961608,0.0005097916,0.7957391,0.0002029304,0.00009182293,0.0001066197,0.0003341848,0.002316175,0.004538656],"genre_scores_gemma":[0.7744915,0.0001606194,0.2207338,0.00005947844,0.00003746624,0.0001160221,0.0003876318,0.00009466706,0.003918746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01252285,"threshold_uncertainty_score":0.0248999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04024688072423022,"score_gpt":0.2799677229152838,"score_spread":0.2397208421910536,"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."}}