{"id":"W4406806559","doi":"10.1007/s12040-024-02483-0","title":"Forecasting of sea surface temperature using machine learning and its applications","year":2025,"lang":"en","type":"article","venue":"Journal of Earth System Science","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sea surface temperature; Surface (topology); Geology; Meteorology; Climatology; Mathematics; Geometry; Physics","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":[0.001025312,0.00007500072,0.0001902581,0.0000701532,0.0003620082,0.00007702494,0.0002185384,0.00003063908,0.00001524562],"category_scores_gemma":[0.0001206801,0.00005370751,0.00003460738,0.001327832,0.0001421243,0.0003948367,0.00001653812,0.0001621535,6.134364e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002583742,"about_ca_system_score_gemma":0.0002728868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000695804,"about_ca_topic_score_gemma":0.00003860914,"domain_scores_codex":[0.9990297,0.00003775207,0.0003273158,0.0001252358,0.0003182096,0.0001617973],"domain_scores_gemma":[0.9990511,0.0001320221,0.0003585309,0.00005306245,0.0003172182,0.00008806407],"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":[0.00001678366,0.000008647925,0.9611644,0.0004164427,0.00001665091,0.00000909825,0.0001111319,0.02772148,0.005725244,0.0001492615,0.000002657626,0.004658269],"study_design_scores_gemma":[0.0003092648,0.000157113,0.03504451,0.0009697308,0.00004568715,0.0005260165,0.001579454,0.9539105,0.006502538,0.00004579253,0.0007614945,0.0001478726],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902443,0.008031865,0.0005122531,0.00003051251,0.0001365222,0.00008295794,0.000007094487,0.000007131239,0.0009474244],"genre_scores_gemma":[0.9946691,0.00009472838,0.005121442,0.00001156084,0.00003281932,5.266424e-8,4.450856e-7,0.000001118849,0.00006868871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9261891,"threshold_uncertainty_score":0.2784311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01402139435498185,"score_gpt":0.2318862525738722,"score_spread":0.2178648582188904,"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."}}