{"id":"W4200408794","doi":"10.3808/jeil.202100074","title":"Outdoor Relative Humidity Prediction via Machine Learning Techniques","year":2021,"lang":"en","type":"article","venue":"Journal of Environmental Informatics Letters","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine learning; Support vector machine; Random forest; Artificial intelligence; Relative humidity; Perceptron; Computer science; Multilayer perceptron; Algorithm; Artificial neural network; Meteorology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005551979,0.0001661529,0.0002249381,0.00005157914,0.0001779923,0.00003835454,0.0001827055,0.00009142921,0.001807139],"category_scores_gemma":[0.0001044763,0.0001406696,0.0001444731,0.0001186616,0.0002402564,0.0008912421,0.0002252644,0.0006839972,0.0001662297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000456227,"about_ca_system_score_gemma":0.000005601306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000574145,"about_ca_topic_score_gemma":0.000001116227,"domain_scores_codex":[0.9982446,0.0001079268,0.0007617074,0.00009588719,0.0005516642,0.000238183],"domain_scores_gemma":[0.9990165,0.00006965498,0.0006409347,0.0001394765,0.000004888871,0.000128552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007651062,0.0004037757,0.1230706,0.00003022962,0.0001408361,0.0002316662,0.003523137,0.05821931,0.7870966,0.000008026397,0.00286928,0.02433007],"study_design_scores_gemma":[0.003378886,0.003016918,0.1529081,0.0003975421,0.0005344046,0.006781385,0.001049888,0.0921366,0.4821201,0.001421681,0.2543344,0.001920033],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783432,0.00002897053,0.01885832,0.0007141748,0.0001634959,0.00007998634,0.00001552267,0.00003482532,0.001761446],"genre_scores_gemma":[0.9633269,0.00006433692,0.03448324,0.001857779,0.00008146219,0.000001703864,0.00002073686,0.00001589845,0.0001479735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3049765,"threshold_uncertainty_score":0.9991053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00956195999105344,"score_gpt":0.2032222606624679,"score_spread":0.1936603006714145,"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."}}