{"id":"W2912697538","doi":"10.1016/j.rser.2019.01.009","title":"Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach","year":2019,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Extreme learning machine; Remote sensing; Environmental science; Random forest; Computer science; Cloud computing; Normalized Difference Vegetation Index; Meteorology; Satellite; Moderate-resolution imaging spectroradiometer; Cloud cover; Data mining; Algorithm; Machine learning; Artificial neural network; Climate change; Geography; Engineering","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":[0.0007869112,0.0004614721,0.0003609099,0.0003413871,0.0003094669,0.0005681523,0.0009030311,0.0004916225,0.0007918533],"category_scores_gemma":[0.002394916,0.0002824291,0.0002400125,0.0004165553,0.000280749,0.001133317,0.0009374177,0.0008233401,0.0002993828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000391786,"about_ca_system_score_gemma":0.0007876644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03186518,"about_ca_topic_score_gemma":0.04214707,"domain_scores_codex":[0.9997453,0.00006244026,0.00001711767,0.00007629495,0.00005614858,0.00004262086],"domain_scores_gemma":[0.9994956,0.0001236164,0.00004361927,0.00009830279,0.0002032079,0.00003564891],"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":[0.0003804411,0.0004322335,0.06266061,0.00006642358,0.0001909264,0.0002482336,0.0003106396,0.5486093,0.01395163,0.002492199,0.003472127,0.3671853],"study_design_scores_gemma":[0.00001310894,0.00003534736,0.007096468,0.00000490578,0.00001537508,0.00001157357,0.00005262222,0.9898242,0.001525186,0.0008282144,0.0005853955,0.000007437796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8004757,0.0003501405,0.19137,0.0004996021,0.0001554353,0.00005380178,0.000290204,0.001439738,0.005365323],"genre_scores_gemma":[0.962508,0.00005375494,0.03552277,0.00004856252,0.00003072478,0.00001588426,0.0002115486,0.00004572541,0.00156302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03186518,"threshold_uncertainty_score":0.06335938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02030517520898729,"score_gpt":0.2327573601051922,"score_spread":0.212452184896205,"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."}}