{"id":"W3048430933","doi":"10.1080/07011784.2020.1796817","title":"Using artificial neural networks to estimate snow water equivalent from snow depth","year":2020,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs; Ministère des Ressources naturelles et des Forêts; Université de Sherbrooke","funders":"","keywords":"Snow; Artificial neural network; Water equivalent; Perceptron; Environmental science; Multilayer perceptron; Meteorology; Mean squared error; Data set; Latitude; Regression; Statistics; Mathematics; Geology; Computer science; Machine learning; Geography; Geodesy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.0004952188,0.0005664874,0.0002559791,0.0006129494,0.000191897,0.0004479433,0.0003675936,0.0003808732,0.0004894963],"category_scores_gemma":[0.001537583,0.0001853554,0.0003019866,0.0005753483,0.0001361332,0.0003911123,0.0002493813,0.0003548976,0.0001406455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006487837,"about_ca_system_score_gemma":0.0005594013,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05555316,"about_ca_topic_score_gemma":0.04455762,"domain_scores_codex":[0.9998628,0.00002994566,0.00001314417,0.00003700021,0.00003280849,0.00002437241],"domain_scores_gemma":[0.9996189,0.0001892092,0.00004619905,0.00001802219,0.0001165752,0.00001102285],"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":[0.000139299,0.00009303283,0.01737542,0.00005655527,0.0001343888,0.00007919288,0.00004506868,0.8351023,0.005846263,0.0002656676,0.0006450286,0.1402178],"study_design_scores_gemma":[0.000002366645,0.000008279346,0.002969242,0.00000357289,0.000008533074,0.000003932541,0.00000696893,0.9960401,0.0007726939,0.00009291694,0.00008770393,0.000003712809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7199546,0.0009470037,0.2733963,0.0001895733,0.0001146971,0.00006167564,0.0004466808,0.001276128,0.003613536],"genre_scores_gemma":[0.9735868,0.0001437983,0.02468207,0.00002518347,0.00001606201,0.00002402469,0.0003342871,0.0000130878,0.001174615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9444469,"threshold_uncertainty_score":0.1104596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05997595861490983,"score_gpt":0.2419921810560915,"score_spread":0.1820162224411817,"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."}}