{"id":"W4232470509","doi":"10.13031/aim.20141890992","title":"Estimation of River Ice-cover Thickness using Bootstrap Artificial Neural Network Models","year":2014,"lang":"en","type":"article","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Robustness (evolution); Linear regression; Variance (accounting); Measure (data warehouse); Cover (algebra); Statistics; Computer science; Environmental science; Mathematics; Machine learning; Data mining; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009157569,0.0004605458,0.0002655825,0.0004773094,0.0001669384,0.0004494939,0.0005710261,0.0004021261,0.0005952209],"category_scores_gemma":[0.002697681,0.0002006895,0.0003501252,0.0004318937,0.0001424693,0.0005107956,0.000297388,0.0003851593,0.0001614888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005240334,"about_ca_system_score_gemma":0.0003771157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01829079,"about_ca_topic_score_gemma":0.02062055,"domain_scores_codex":[0.9998374,0.00007140841,0.0000103752,0.00003058073,0.00003529478,0.0000149492],"domain_scores_gemma":[0.9992041,0.0004639682,0.00009348524,0.00004245608,0.0001775692,0.00001844594],"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":[0.00005982385,0.00004122639,0.01104315,0.00002643019,0.0000561233,0.00003068922,0.00001325801,0.9598386,0.001439274,0.0002692627,0.0002051249,0.02697706],"study_design_scores_gemma":[0.000001093095,0.000005088986,0.001680882,0.000002850682,0.00000255067,0.0000025193,0.00000294936,0.9979078,0.0002321302,0.0001255364,0.00003453626,0.000002144016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7067423,0.0005061252,0.2885905,0.0001803308,0.00004903389,0.00003792896,0.000464749,0.0006728424,0.002756287],"genre_scores_gemma":[0.9759933,0.00009307008,0.02300013,0.00002004518,0.000009334914,0.00002654217,0.0003087083,0.00001867812,0.0005301466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01829079,"threshold_uncertainty_score":0.03636867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07001673551525567,"score_gpt":0.2527775997736751,"score_spread":0.1827608642584194,"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."}}