{"id":"W4380686525","doi":"10.5194/gmd-16-3335-2023","title":"Adding sea ice effects to a global operational model (NEMO v3.6) for forecasting total water level: approach and impact","year":2023,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Sea ice; Arctic ice pack; Climatology; Drift ice; Antarctic sea ice; Fast ice; Hindcast; Sea ice thickness; Lead (geology); Environmental science; Sea ice concentration; Geology; Oceanography; Geomorphology","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":[0.0003636903,0.0006983412,0.0003549402,0.0002005291,0.0001844441,0.0004781145,0.0005801387,0.0003901205,0.000975028],"category_scores_gemma":[0.0005723824,0.0001927034,0.0003803841,0.0001918496,0.00016054,0.0004791664,0.0004337007,0.0004390179,0.0001752992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003004135,"about_ca_system_score_gemma":0.0004886495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01209338,"about_ca_topic_score_gemma":0.01263202,"domain_scores_codex":[0.9999309,0.00002363344,0.000005292609,0.0000123561,0.00001873057,0.000009144077],"domain_scores_gemma":[0.9998412,0.00004924091,0.0000189081,0.00003072632,0.00004092748,0.00001891413],"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":[0.0001033063,0.00006134508,0.01300011,0.0000491534,0.00008407234,0.00005563084,0.00002313715,0.9635902,0.004360101,0.0003999523,0.0005496308,0.0177234],"study_design_scores_gemma":[0.000009142495,0.00002886244,0.001577569,0.000003739045,0.00001299261,0.000003992179,0.000007199072,0.9967856,0.001071535,0.0001264135,0.0003677314,0.000005123859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8978809,0.0003684844,0.08977996,0.0003109563,0.0002094069,0.0001125516,0.002449249,0.00209099,0.006797581],"genre_scores_gemma":[0.9808912,0.000117717,0.01720688,0.00003597494,0.00002231644,0.00004533416,0.0008506263,0.00008987558,0.0007400487],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01209338,"threshold_uncertainty_score":0.02404594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04898927951514825,"score_gpt":0.2494547347685053,"score_spread":0.200465455253357,"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."}}