{"id":"W2083950122","doi":"10.1007/s40314-014-0208-x","title":"Statistical optimization with nonlinear constraints and parameter identification for the cutoff height of pressure ridges","year":2014,"lang":"en","type":"article","venue":"Computational and Applied Mathematics","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Chinese Arctic and Antarctic Administration; York University","keywords":"Cutoff; Ridge; Cutoff frequency; Nonlinear system; Mathematics; Geology; Computer simulation; Statistics; Physics; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001540515,0.00006291557,0.00009803486,0.00001344599,0.0001159755,0.00003101727,0.00004189905,0.00002201073,0.00003582972],"category_scores_gemma":[0.00003736708,0.00003736265,0.0000080535,0.00002981576,0.0002696784,0.00003189156,0.000005529051,0.00003351959,0.000001066532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":4.612147e-7,"about_ca_system_score_gemma":0.0000127971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003126675,"about_ca_topic_score_gemma":0.000002183093,"domain_scores_codex":[0.9995686,0.000006995991,0.0001492992,0.00009824785,0.0001108159,0.00006603214],"domain_scores_gemma":[0.9984584,0.001326473,0.00009205301,0.00004803686,0.00004717914,0.00002786278],"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":[0.00006500449,0.00005680609,0.003279163,0.0005099876,0.00009890804,1.581649e-7,0.0008115481,0.7890462,0.000006725489,0.1430863,0.00008554672,0.06295361],"study_design_scores_gemma":[0.0002191297,0.00003941982,0.01007369,0.00001159532,0.0000628721,0.000008997956,0.0001959555,0.9652377,0.000006947103,0.02399321,0.00009577019,0.00005471158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02177359,0.00002619206,0.9774772,0.0001591208,0.00001553654,0.0002187423,0.0001339783,0.000007091453,0.0001885808],"genre_scores_gemma":[0.6269353,0.000008963905,0.3728587,0.00004461067,0.00001629674,0.000002298667,0.0001205101,0.00000179328,0.00001151806],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6051617,"threshold_uncertainty_score":0.1523605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009262545030807763,"score_gpt":0.2083057846049429,"score_spread":0.1990432395741352,"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."}}