{"id":"W4390270779","doi":"10.22541/essoar.170365288.82165749/v1","title":"Deep Graph Neural Networks for Spatiotemporal Forecasting of Sub-Seasonal Sea Ice: A Case Study in Hudson Bay","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"National Research Council Canada; Alliance de recherche numérique du Canada","keywords":"Bay; Graph; Sea ice; Kernel (algebra); Computer science; Representation (politics); Sequence (biology); Artificial neural network; Artificial intelligence; Oceanography; Climatology; Geology; Mathematics; Theoretical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004030387,0.0005425696,0.0002524636,0.0003885729,0.0002565251,0.0004431021,0.0006713815,0.0004376001,0.0007728835],"category_scores_gemma":[0.0007971826,0.000165574,0.0002679688,0.0004866716,0.000297082,0.0004988049,0.0003307822,0.0005898778,0.000102752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001725457,"about_ca_system_score_gemma":0.0008254778,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.188525,"about_ca_topic_score_gemma":0.2130443,"domain_scores_codex":[0.9999331,0.0000162652,0.00000423874,0.00001888359,0.00001193107,0.00001549445],"domain_scores_gemma":[0.99979,0.0001046117,0.00001769176,0.00001418062,0.00004801364,0.00002557457],"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":[0.0001773201,0.0001073926,0.02760602,0.00005236975,0.00003964123,0.0007533369,0.00009202281,0.9335848,0.001221548,0.0008311008,0.002342463,0.0331919],"study_design_scores_gemma":[0.000006410848,0.00002293399,0.003020369,0.000003672092,0.000005357281,0.00001173691,0.00007307157,0.9956309,0.0005470326,0.0004032145,0.0002725876,0.000002850703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893771,0.0002161987,0.007350631,0.0004879824,0.00004292609,0.00002495323,0.0008347697,0.0003482178,0.00131717],"genre_scores_gemma":[0.9924054,0.0001033422,0.00579092,0.00003479068,0.000005661717,0.000007289262,0.0006062515,0.00001359015,0.001032758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.811475,"threshold_uncertainty_score":0.3748553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04940700285546,"score_gpt":0.2642253200568975,"score_spread":0.2148183172014375,"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."}}