{"id":"W2050920748","doi":"10.1080/08839510802226785","title":"ENSEMBLE ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF DEW POINT TEMPERATURE","year":2008,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Dew point; Artificial neural network; Computer science; Dew; Point (geometry); Statistics; Meteorology; Machine learning; Mathematics","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":[0.001014661,0.0008056425,0.0008024429,0.0007364369,0.0002831965,0.000561872,0.000556197,0.0004540806,0.0004825302],"category_scores_gemma":[0.002130134,0.0003429613,0.0007040419,0.0007036945,0.00009212689,0.0007666624,0.0004075081,0.001034916,0.0002067824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005718932,"about_ca_system_score_gemma":0.0004984332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01595185,"about_ca_topic_score_gemma":0.01752539,"domain_scores_codex":[0.9995993,0.0000694407,0.00003235366,0.0001088859,0.0001543037,0.0000356314],"domain_scores_gemma":[0.9992995,0.0002462378,0.00007682564,0.00005734685,0.0002977575,0.00002220935],"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":[0.00007606565,0.00007312037,0.007369899,0.0000315699,0.0001178308,0.0000444459,0.00002717636,0.8911672,0.002823293,0.0002398069,0.0007294788,0.09730013],"study_design_scores_gemma":[8.751556e-7,0.000008434791,0.001097467,0.000002170189,0.000006180094,0.000003287214,0.000002772955,0.9982572,0.0004242714,0.00007915463,0.0001153408,0.000002816875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4668382,0.002228237,0.5237026,0.0002821438,0.0003382295,0.00007695352,0.0009501726,0.001808322,0.003775221],"genre_scores_gemma":[0.9451954,0.0005471226,0.05108676,0.00003866995,0.00003795594,0.00005415592,0.0009393825,0.00004137956,0.002059177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01595185,"threshold_uncertainty_score":0.03171796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06637134082270504,"score_gpt":0.2664963277113853,"score_spread":0.2001249868886803,"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."}}