{"id":"W3041052911","doi":"10.3390/w12071973","title":"Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Universidad de La Sabana; Pontificia Universidad Javeriana; Universidad Tecnológica de Bolívar","keywords":"Artificial neural network; Entropy (arrow of time); Computer science; Probabilistic logic; Acronym; Meteorology; Artificial intelligence; Data mining; Environmental science; Geography","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":[0.0005584155,0.0004494636,0.0002988985,0.002009277,0.0003735016,0.0009450755,0.0004749584,0.0004428973,0.0006193196],"category_scores_gemma":[0.00218582,0.0001170752,0.0002725779,0.001947265,0.000356816,0.000452511,0.0004275377,0.0002250772,0.00004637717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002468696,"about_ca_system_score_gemma":0.0005723516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1357037,"about_ca_topic_score_gemma":0.1536338,"domain_scores_codex":[0.9995956,0.0001651895,0.00003240878,0.00004611011,0.0001086751,0.00005201499],"domain_scores_gemma":[0.9990403,0.0005256358,0.0000891221,0.00004564117,0.0002434571,0.00005585656],"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":[0.0006550923,0.0004096788,0.3517644,0.0008839876,0.0002921661,0.007954109,0.002406712,0.4666516,0.007314993,0.005689937,0.002442535,0.1535347],"study_design_scores_gemma":[0.00005670973,0.0002313325,0.2970867,0.0001187824,0.0001281345,0.0005488871,0.006317614,0.6845297,0.005002121,0.001783823,0.004076467,0.0001197096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988664,0.000330157,0.005546954,0.000222395,0.00001356124,0.00007661895,0.0006206085,0.00006398615,0.004461819],"genre_scores_gemma":[0.9969717,0.000100278,0.002360757,0.000005882752,0.000003865752,0.00001101688,0.0001678025,0.000003798204,0.0003746848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1357037,"threshold_uncertainty_score":0.2698276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02258828426636466,"score_gpt":0.2439907132812223,"score_spread":0.2214024290148576,"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."}}