{"id":"W2018567030","doi":"10.1002/env.912","title":"A spatio‐temporal model for Antarctic sea ice formation","year":2008,"lang":"en","type":"article","venue":"Environmetrics","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; University of Waterloo; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Centre for Interdisciplinary Research in Music Media and Technology; McGill University","keywords":"Sea ice; Sea ice concentration; Climatology; Drift ice; Variation (astronomy); Arctic ice pack; Antarctic sea ice; Series (stratigraphy); Sea ice thickness; Environmental science; Cryosphere; Oceanography; Geology; Atmospheric sciences; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008657793,0.0004988498,0.0003626756,0.0007766833,0.0005901986,0.001488855,0.001858697,0.001414357,0.007264948],"category_scores_gemma":[0.002098458,0.0004645762,0.0008930665,0.0009515316,0.0009201036,0.001230712,0.0008367259,0.001007168,0.001200048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00174747,"about_ca_system_score_gemma":0.00118739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04249195,"about_ca_topic_score_gemma":0.02375142,"domain_scores_codex":[0.9998283,0.00005834916,0.00001029562,0.00004776607,0.00002773935,0.00002753479],"domain_scores_gemma":[0.999458,0.0002402047,0.0001176907,0.00004524845,0.00007599371,0.00006278818],"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":[0.00002942238,0.00001966156,0.002091247,0.00001599043,0.00002999613,0.0000648693,0.00006514377,0.9629371,0.0003640916,0.03154959,0.0008709966,0.001961979],"study_design_scores_gemma":[0.00000753632,0.000005434745,0.0003801423,0.000003234158,0.000005627013,0.0000164976,0.00001008272,0.9944203,0.00002186513,0.004304191,0.0008209751,0.000004032282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.439822,0.001541191,0.4938023,0.005296931,0.0003558704,0.0001863517,0.006861783,0.001005415,0.05112822],"genre_scores_gemma":[0.9633594,0.0006045939,0.01486987,0.0001338952,0.00009221659,0.0003029522,0.0009756948,0.0001169468,0.01954444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04249195,"threshold_uncertainty_score":0.08448923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02689970499011934,"score_gpt":0.201443168878891,"score_spread":0.1745434638887717,"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."}}