{"id":"W2981196952","doi":"","title":"Extreme Learning Machines for Environmental Problems","year":2013,"lang":"en","type":"article","venue":"93rd American Meteorological Society Annual Meeting","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence","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":[0.001692793,0.0006076197,0.001226661,0.0007729874,0.0004989072,0.001214469,0.001352529,0.001744199,0.003736472],"category_scores_gemma":[0.006420076,0.000389927,0.0007181663,0.001379079,0.0009719409,0.001472579,0.001646269,0.003115848,0.0007018619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005384802,"about_ca_system_score_gemma":0.0005303144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001552513,"about_ca_topic_score_gemma":0.001493687,"domain_scores_codex":[0.999247,0.0004197104,0.0000386347,0.00009848437,0.0001499628,0.00004626147],"domain_scores_gemma":[0.9973541,0.002041699,0.0001397619,0.0001771652,0.0002208735,0.0000663268],"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":[0.00005284756,0.00007260811,0.0005691019,0.0001982276,0.00009137773,0.00007158311,0.00007233518,0.7057893,0.0004555086,0.1336838,0.009005006,0.1499383],"study_design_scores_gemma":[0.00001144217,0.00001032725,0.000135504,0.00001291226,0.000005731509,0.00001049341,0.000006154761,0.8177242,0.0000928865,0.179741,0.002243844,0.000005395347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0065605,0.003596622,0.9842694,0.0009879636,0.0001685281,0.00002230636,0.00007916265,0.0002231281,0.004092368],"genre_scores_gemma":[0.4976603,0.004827586,0.4711301,0.0006177592,0.001466923,0.0004131124,0.0008194172,0.0002919151,0.02277286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003736472,"threshold_uncertainty_score":0.01249975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01332123744720106,"score_gpt":0.2273657315388324,"score_spread":0.2140444940916313,"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."}}