{"id":"W2890514030","doi":"10.5194/tc-2018-178","title":"IcePAC – a Probabilistic Tool to Study Sea Ice Spatiotemporal Dynamic: Application to the Hudson Bay area, Northeastern Canada","year":2018,"lang":"en","type":"article","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Center for Northern Studies; Institut National de la Recherche Scientifique","funders":"European Organization for the Exploitation of Meteorological Satellites; Université de Moncton; Natural Resources Canada; Université du Québec à Rimouski","keywords":"Sea ice; Bay; Sea ice concentration; Meteorology; Climatology; Sea ice thickness; Cryosphere; Oceanography; Geography; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0006264025,0.0004163988,0.0001769713,0.001200051,0.0004866922,0.0006620816,0.0004604867,0.0001914328,0.001460999],"category_scores_gemma":[0.001366493,0.0001476058,0.0002877418,0.001375522,0.0002390036,0.0002823809,0.0003716429,0.000181723,0.000142177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003663867,"about_ca_system_score_gemma":0.004692529,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8899571,"about_ca_topic_score_gemma":0.858973,"domain_scores_codex":[0.9998398,0.00002870878,0.000008965644,0.00004035147,0.00006157654,0.00002055563],"domain_scores_gemma":[0.9995325,0.0001662694,0.00004036408,0.00003319882,0.0001899451,0.00003781021],"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":[0.0002069278,0.0001209101,0.1718841,0.0001283166,0.0001246355,0.0003701743,0.0003358514,0.7144801,0.002275986,0.003519419,0.00624794,0.1003057],"study_design_scores_gemma":[0.00001274293,0.00002564204,0.0449848,0.00001362605,0.00001618054,0.00006390159,0.0002113495,0.9494923,0.0005907004,0.0004869434,0.004086595,0.00001520734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8761888,0.0005106369,0.09231994,0.0003428819,0.00003431898,0.0002914691,0.0129082,0.003637496,0.01376629],"genre_scores_gemma":[0.946291,0.0002055286,0.04729544,0.00002704986,0.000007683372,0.00006892644,0.004145639,0.00009234554,0.001866426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1100429,"threshold_uncertainty_score":0.2213818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008111893869352044,"score_gpt":0.2134162730565054,"score_spread":0.2053043791871533,"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."}}