{"id":"W2104997636","doi":"10.5194/gmd-9-2255-2016","title":"The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1","year":2016,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Deutsche Forschungsgemeinschaft; Natural Environment Research Council; Sight Research UK","keywords":"Predictability; Climatology; Arctic; Environmental science; Sea ice; Arctic ice pack; Climate model; Forecast skill; Climate change; Meteorology; Oceanography; Geography; Geology; Statistics; Mathematics","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":[0.001228523,0.0001639795,0.0001122952,0.00005799517,0.0008954459,0.0001276338,0.0005114088,0.00005008749,0.0001921394],"category_scores_gemma":[0.0001271681,0.00008857468,0.00002229336,0.000134029,0.0002204547,0.00028449,0.0001862956,0.00009563835,0.0003431758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003583044,"about_ca_system_score_gemma":0.0001715666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009259154,"about_ca_topic_score_gemma":0.0004089344,"domain_scores_codex":[0.998018,0.00006767222,0.0002683752,0.0006821496,0.0005636265,0.000400209],"domain_scores_gemma":[0.9987925,0.0002543534,0.00005759742,0.0005876154,0.00007407166,0.0002338874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006474013,0.0001019465,0.4794419,0.00008814983,0.00008302076,0.00000908934,0.003002044,0.004296661,0.0001108958,0.0001947559,0.02726789,0.4847563],"study_design_scores_gemma":[0.0003562517,0.00008241642,0.5146403,0.0001389016,0.0000207749,0.00001493547,0.0002224888,0.4525708,0.00003308394,0.0005003524,0.03119155,0.0002281601],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9613371,0.00003170666,0.03252114,0.002145254,0.000941496,0.0003660615,0.002052308,0.00006147425,0.0005434781],"genre_scores_gemma":[0.9915891,0.00004948048,0.003297166,0.0001898541,0.00003985406,0.000003640345,0.0005793565,0.000004364411,0.004247166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4845281,"threshold_uncertainty_score":0.6887137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02200356079309797,"score_gpt":0.2174595646650476,"score_spread":0.1954560038719496,"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."}}