{"id":"W7117371263","doi":"10.1016/j.scsadv.2025.100022","title":"Interpretable generalized Gaussian mixture modeling for risk-aware solar power forecasting","year":2025,"lang":"en","type":"article","venue":"Sustainable Cities and Society Advances","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Probabilistic logic; Heteroscedasticity; Probabilistic forecasting; Robustness (evolution); Mixture model; Gaussian; Parameterized complexity; Gaussian process; Bayesian probability","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001714566,0.00120258,0.000908617,0.0007358312,0.0003215221,0.0009703754,0.00152098,0.001181145,0.001278541],"category_scores_gemma":[0.005121194,0.0005766109,0.001013415,0.0009272626,0.0006164432,0.001433768,0.0009315201,0.002054752,0.0004018116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008695103,"about_ca_system_score_gemma":0.0007656555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0102782,"about_ca_topic_score_gemma":0.01099097,"domain_scores_codex":[0.9995311,0.0002098903,0.00002319892,0.0001066763,0.00008524398,0.00004392615],"domain_scores_gemma":[0.9987481,0.0008395395,0.000134514,0.0001093889,0.0001297061,0.00003881771],"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":[0.00004202572,0.00001556498,0.0008409161,0.00002365972,0.00004129116,0.00002933149,0.00003884943,0.9667296,0.000476681,0.009289648,0.0007068522,0.02176543],"study_design_scores_gemma":[0.00000162558,0.000003393458,0.00008990405,0.000002760577,0.000003616929,0.00000448629,0.00000197038,0.9926203,0.00008647802,0.007026253,0.0001556949,0.000003495309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0150454,0.0004484473,0.982677,0.0003299775,0.00004679233,0.00001608701,0.0002096487,0.0005736429,0.0006529411],"genre_scores_gemma":[0.81521,0.0009163523,0.1793624,0.0002356318,0.0001992823,0.0001187687,0.001262043,0.0002019718,0.002493323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0102782,"threshold_uncertainty_score":0.02043676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007953765955038711,"score_gpt":0.2416686854048815,"score_spread":0.2337149194498428,"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."}}