{"id":"W3203762460","doi":"","title":"Seasonal Sea Ice Presence Forecasting of Hudson Bay using Seq2Seq Learning","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bay; Sea ice; Oceanography; Climatology; Environmental science; Meteorology; Geology; Geography","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":[0.0004747346,0.0004590687,0.0003570167,0.0007043966,0.0002560924,0.0004007089,0.0005064324,0.0003379715,0.001126628],"category_scores_gemma":[0.0008317857,0.0001924782,0.0003600615,0.0006057186,0.0001486086,0.0003812539,0.0003758812,0.0003821634,0.0004255926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003677895,"about_ca_system_score_gemma":0.0006541967,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07170095,"about_ca_topic_score_gemma":0.09761306,"domain_scores_codex":[0.999889,0.00001462605,0.000008494524,0.00004601579,0.0000171307,0.00002471929],"domain_scores_gemma":[0.9996535,0.0001001143,0.00002756252,0.00002947654,0.0001301302,0.00005934493],"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":[0.001265092,0.0004334098,0.497202,0.0001121563,0.0002516581,0.0005515738,0.0002175306,0.3476312,0.01117033,0.0003749816,0.01767164,0.1231183],"study_design_scores_gemma":[0.00002383171,0.00004564891,0.07634089,0.000007843629,0.00003236486,0.00002532058,0.00009769921,0.9211885,0.001302691,0.0001272418,0.0007953813,0.00001256806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901934,0.0002665965,0.003578765,0.0001980253,0.0001676412,0.00001217661,0.004242033,0.0003057346,0.001035636],"genre_scores_gemma":[0.9898488,0.00006226154,0.002812105,0.00003198644,0.00003363239,0.000007100918,0.006429972,0.00001545862,0.0007587309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9282991,"threshold_uncertainty_score":0.1425672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05208139374302485,"score_gpt":0.2776881007093523,"score_spread":0.2256067069663275,"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."}}