{"id":"W4221132627","doi":"10.5194/egusphere-egu22-7219","title":"Evaluating the skill of seasonal forecasts of sea ice in the Southern Ocean: insights from the SIPN South project 2017-2022","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Climatology; Sea ice; Initialization; Forecast skill; Circumpolar star; Meteorology; Environmental science; Coupled model intercomparison project; Range (aeronautics); Oceanography; Geography; Computer science; Geology; Climate model; Climate change; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004137599,0.0005937486,0.0003625063,0.0007292434,0.0001982893,0.0009903513,0.0004729793,0.0005453777,0.0005390273],"category_scores_gemma":[0.008614758,0.0001654288,0.0005092259,0.0006889107,0.0003294836,0.0007596235,0.000929737,0.0005192923,0.0002785789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004790312,"about_ca_system_score_gemma":0.0007244463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01867949,"about_ca_topic_score_gemma":0.01177836,"domain_scores_codex":[0.9993624,0.0002237553,0.00004262681,0.0001151554,0.0002028896,0.00005321455],"domain_scores_gemma":[0.9958882,0.001981375,0.0004615608,0.0003691907,0.001034993,0.0002646773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008136733,0.0002864403,0.4983544,0.0003488604,0.0006552109,0.0003980233,0.0007537498,0.4162849,0.003715718,0.001524738,0.01367856,0.06318572],"study_design_scores_gemma":[0.00009772843,0.0003778372,0.3317543,0.0001697525,0.0001593877,0.0001284238,0.0007931113,0.6483897,0.006120844,0.002122594,0.009799143,0.00008716551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895362,0.000490548,0.002299205,0.0004801849,0.00006391502,0.00001742592,0.004411569,0.0001558009,0.002545154],"genre_scores_gemma":[0.9847133,0.0002094142,0.001736871,0.0000274921,0.0000394727,0.00001556755,0.01282041,0.00007181171,0.0003656797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01867949,"threshold_uncertainty_score":0.03714156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05217035870735737,"score_gpt":0.2884101882786424,"score_spread":0.236239829571285,"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."}}