{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002994384,0.000452318,0.0003677249,0.0007503683,0.0001744751,0.0004763585,0.0003846652,0.00043382,0.0009591756],"category_scores_gemma":[0.001097624,0.0001566452,0.0003833806,0.000703571,0.0001334379,0.0005696372,0.0002310811,0.0004767144,0.0003933488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002692609,"about_ca_system_score_gemma":0.0002957636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004947394,"about_ca_topic_score_gemma":0.003972127,"domain_scores_codex":[0.9998462,0.00003041325,0.00001037882,0.00004783709,0.00004621709,0.00001886492],"domain_scores_gemma":[0.9997395,0.0001399502,0.00003771766,0.00001932903,0.0000559085,0.000007665344],"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":[0.00008668649,0.00009799858,0.005876283,0.00009695019,0.00006715467,0.00007712607,0.00004653323,0.6202589,0.008730571,0.001412721,0.001550502,0.3616985],"study_design_scores_gemma":[0.000001506982,0.000009474041,0.0007997531,0.000003782916,0.000003504947,0.000008356077,0.000003990548,0.9975079,0.001065437,0.0003780918,0.0002152752,0.000002748977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1919561,0.001275909,0.796298,0.0003358853,0.000162913,0.00005098167,0.0003539667,0.002674901,0.006891419],"genre_scores_gemma":[0.9328525,0.0003444558,0.06451843,0.00004183013,0.00007199428,0.00003851426,0.0002491676,0.00003555821,0.00184753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004947394,"threshold_uncertainty_score":0.00983721,"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."}}