{"id":"W2015903360","doi":"10.1016/j.coldregions.2012.02.006","title":"An idealised stochastic model of sea ice thickness dynamics","year":2012,"lang":"en","type":"article","venue":"Cold Regions Science and Technology","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Canadian Foundation for Climate and Atmospheric Sciences; ArcticNet","keywords":"Redistribution (election); Exponential function; Sea ice; Statistical physics; Mechanics; Rheology; Stochastic modelling; Exponential distribution; Geology; Physics; Mathematics; Thermodynamics; Mathematical analysis; Climatology; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001646763,0.0007436657,0.001805878,0.0008521996,0.0007683987,0.002476421,0.002758712,0.004100776,0.003439102],"category_scores_gemma":[0.006368399,0.001135475,0.00104519,0.001146249,0.002904558,0.002864758,0.001563635,0.002153285,0.0004910013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00205514,"about_ca_system_score_gemma":0.002069298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02657743,"about_ca_topic_score_gemma":0.01023136,"domain_scores_codex":[0.9991677,0.0003423962,0.00003996283,0.0001775932,0.0001159942,0.0001562963],"domain_scores_gemma":[0.9974377,0.001390692,0.0003594676,0.0001490664,0.0003725101,0.0002904698],"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":[0.00002777038,0.00001153183,0.000238936,0.00001033159,0.00001179617,0.00003813315,0.00001407616,0.9846191,0.0001349842,0.01431576,0.0002626968,0.0003149711],"study_design_scores_gemma":[0.00001469124,0.000004577347,0.00009354327,0.000002211467,0.000004739677,0.000007229763,0.000004314257,0.995128,0.00002028767,0.00459293,0.0001196099,0.000007922135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3775077,0.001410016,0.5737776,0.00628504,0.0007426508,0.0001067646,0.002949997,0.0007518274,0.03646848],"genre_scores_gemma":[0.9716091,0.0003694056,0.009420283,0.0003071432,0.0001881255,0.00009096468,0.0005060901,0.00008994608,0.01741886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02657743,"threshold_uncertainty_score":0.05284548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01462094775009797,"score_gpt":0.2303886113452096,"score_spread":0.2157676635951117,"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."}}