{"id":"W4281864822","doi":"10.5194/tc-2022-110","title":"Impact of atmospheric forcing uncertainties on Arctic and Antarctic sea ice simulation in CMIP6 OMIP","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou); National Natural Science Foundation of China","keywords":"Sea ice; Climatology; Arctic ice pack; Sea ice concentration; Arctic; Environmental science; Sea ice thickness; Cryosphere; Drift ice; Arctic sea ice decline; Antarctic sea ice; Arctic geoengineering; Atmospheric sciences; Geology; Oceanography","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":[0.002130787,0.001314293,0.0005019681,0.000494673,0.0007298607,0.00106352,0.00112363,0.001405957,0.001114449],"category_scores_gemma":[0.004141344,0.0004160136,0.0009594896,0.000940054,0.0004184573,0.0008407977,0.0007551432,0.0008184349,0.0002002111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001571146,"about_ca_system_score_gemma":0.001298066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08940111,"about_ca_topic_score_gemma":0.03360286,"domain_scores_codex":[0.9994895,0.0002179576,0.00003364604,0.0001130201,0.0000585931,0.00008721659],"domain_scores_gemma":[0.9984099,0.0009041441,0.0001525487,0.0001300254,0.0002891161,0.0001142793],"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":[0.00041338,0.0001138772,0.08538195,0.00008876304,0.0002643281,0.0002672591,0.00005200112,0.9034878,0.001514047,0.0006406299,0.001610331,0.006165633],"study_design_scores_gemma":[0.0001312524,0.0001133696,0.04240377,0.00005318301,0.0001331246,0.00004314809,0.0001316928,0.9528032,0.002869997,0.0003097577,0.000967988,0.00003951103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927914,0.0003598086,0.001133886,0.0004336443,0.00006969853,0.00001950808,0.002765015,0.000152253,0.002274638],"genre_scores_gemma":[0.9979353,0.00008262385,0.000577717,0.00005904467,0.00001398283,0.00001868039,0.001130877,0.00002226928,0.0001595621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08940111,"threshold_uncertainty_score":0.1777615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01656438564128781,"score_gpt":0.2626766994813596,"score_spread":0.2461123138400718,"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."}}