{"id":"W2155523560","doi":"10.1002/grl.50129","title":"Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system","year":2013,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Toronto","funders":"University of Toronto","keywords":"Forecast skill; Climatology; Lead (geology); Environmental science; Anomaly (physics); Sea ice; Lead time; Arctic; Arctic ice pack; Forecast period; Lag; Meteorology; Geology; Geography; Oceanography; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006385217,0.0002107747,0.0003686615,0.0002243441,0.0001687881,0.00008209773,0.0005149968,0.00009705699,0.0006111822],"category_scores_gemma":[0.0002548912,0.0001698635,0.0001460832,0.0007492364,0.000619862,0.0003767768,0.00008480888,0.0007940585,0.0007653487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008032263,"about_ca_system_score_gemma":0.0001121319,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02639813,"about_ca_topic_score_gemma":0.001301817,"domain_scores_codex":[0.9965124,0.0003308542,0.0003738862,0.0004618552,0.001207438,0.001113566],"domain_scores_gemma":[0.9978155,0.001224716,0.00007653794,0.0003326762,0.0001992824,0.0003512453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003499504,0.0003151982,0.9275055,0.0007794644,0.0001061205,0.0002528322,0.001133648,0.002176984,0.001229955,0.002500048,0.002304216,0.06134611],"study_design_scores_gemma":[0.0004405879,0.0001998057,0.6030395,0.0002168489,0.000008214572,0.00002484988,0.001117682,0.3938278,0.00001771138,0.0008353314,0.00006826303,0.0002034492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941979,0.00001706312,0.0004649259,0.002696943,0.000116608,0.0004943885,0.00008188353,0.00002823514,0.001902019],"genre_scores_gemma":[0.9985206,0.000007235704,0.0007336545,0.0003141906,0.0001699311,0.00001795157,0.0001238486,0.00001063004,0.0001020004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3916508,"threshold_uncertainty_score":0.9837264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02112795872092133,"score_gpt":0.246436084793149,"score_spread":0.2253081260722276,"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."}}