{"id":"W4306250297","doi":"10.1002/essoar.10512617.1","title":"Seasonal predictability of summer melt ponds from winter sea ice surface temperature","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":"McGill University","funders":"Norges Forskningsråd; Nuclear Safety and Security Commission; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; National Aeronautics and Space Administration","keywords":"Snow; Sea ice; Climatology; Arctic ice pack; Melt pond; Environmental science; Atmospheric sciences; Arctic; Antarctic sea ice; Cryosphere; 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":[0.0001782772,0.0001664636,0.000189864,0.0004851542,0.0001631638,0.0004218008,0.00007290862,0.0001677164,0.0007233098],"category_scores_gemma":[0.0004694698,0.0001172181,0.000183075,0.0003116603,0.000136164,0.0002144708,0.0001656793,0.000120612,0.0001793052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001791801,"about_ca_system_score_gemma":0.0001231244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008250752,"about_ca_topic_score_gemma":0.01234301,"domain_scores_codex":[0.9999589,0.000005681995,0.000002819005,0.00001339255,0.000006332204,0.00001286087],"domain_scores_gemma":[0.9997724,0.00007395828,0.00005846298,0.00001584986,0.00003966543,0.00003963589],"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":[0.0003395589,0.00002988544,0.9558365,0.00002546835,0.00005381294,0.0001086402,0.0001633623,0.01381917,0.01402613,0.0001109533,0.0007484033,0.01473817],"study_design_scores_gemma":[0.000005735234,0.00002586807,0.9648756,0.000005041175,0.00001008423,0.0000415738,0.00005960467,0.03331199,0.001263498,0.0001086957,0.0002871948,0.000005219047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988937,0.00003803874,0.0003461193,0.00001424658,0.000002790344,0.000001085545,0.0003530422,0.00005737968,0.0002937076],"genre_scores_gemma":[0.9992249,0.00001352077,0.0001053646,0.000001599251,0.000002356513,0.000001024787,0.0005511658,0.000006627745,0.0000934378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008250752,"threshold_uncertainty_score":0.01640546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01313193332191844,"score_gpt":0.225860254202574,"score_spread":0.2127283208806555,"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."}}