{"id":"W4407674905","doi":"10.1080/1755876x.2024.2447155","title":"Tuning ice model parameters to improve Arctic sea-ice simulation using the ERA5 atmospheric reanalysis forcing","year":2025,"lang":"en","type":"article","venue":"Journal of Operational Oceanography","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"","keywords":"Sea ice; Forcing (mathematics); Climatology; Environmental science; Arctic; The arctic; Sea ice thickness; Arctic ice pack; Atmospheric model; Cryosphere; Sea ice concentration; Atmospheric sciences; Geology; Meteorology; Oceanography; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006768293,0.0001468887,0.0002199261,0.0001286696,0.0005431893,0.0001903055,0.0002541526,0.00004893668,0.00005955186],"category_scores_gemma":[0.0001242013,0.00009968217,0.0002388294,0.0009251086,0.0000622123,0.0005885888,0.00001761053,0.0002590074,0.000002437594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002673865,"about_ca_system_score_gemma":0.0001890329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001803738,"about_ca_topic_score_gemma":0.00005350381,"domain_scores_codex":[0.9985496,0.00009152549,0.0005180005,0.0001644311,0.0004572201,0.0002192331],"domain_scores_gemma":[0.9986774,0.0004468106,0.0002411237,0.0001481282,0.0003829434,0.000103561],"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":[0.0000687103,0.00001080886,0.1541428,0.000009362598,0.0001309149,0.000002868248,0.0002952665,0.8432674,0.00005849451,0.00009586503,0.00004823253,0.001869222],"study_design_scores_gemma":[0.000219193,0.00007109607,0.02294609,0.0000656801,0.0001627067,0.00001477328,0.0005384065,0.9749632,0.000008966354,0.0007413598,0.000155073,0.0001134643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6645605,0.0001311909,0.3339541,0.0008739437,0.0002148714,0.0001017139,0.00001046524,0.00000540182,0.000147838],"genre_scores_gemma":[0.9157222,0.00003642269,0.08201832,0.002065536,0.00009081231,2.726364e-7,0.00001121198,0.000004042256,0.00005119237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2519358,"threshold_uncertainty_score":0.4177828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506376926673247,"score_gpt":0.2521400529292837,"score_spread":0.2370762836625512,"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."}}