{"id":"W4401970802","doi":"10.14710/ik.ijms.29.2.273-284","title":"Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio","year":2024,"lang":"en","type":"article","venue":"ILMU KELAUTAN Indonesian Journal of Marine Sciences","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Current (fluid); Oceanography; East coast; Meteorology; Ocean current; Environmental science; Algorithm; Climatology; Geography; Geology; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001374856,0.0002020226,0.0003290907,0.0001668424,0.0003822586,0.0002298474,0.0006741658,0.00004821379,0.00001535855],"category_scores_gemma":[0.0001222317,0.0001072428,0.00007599519,0.0009257749,0.002282185,0.0005341358,0.0004922518,0.0004821389,0.000001338959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008009915,"about_ca_system_score_gemma":0.00008559216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001739039,"about_ca_topic_score_gemma":0.00005170675,"domain_scores_codex":[0.997925,0.0001242969,0.0005233914,0.000287576,0.0008321339,0.0003076209],"domain_scores_gemma":[0.9991063,0.0001914453,0.0003907687,0.0001826248,0.0000393895,0.00008948386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000831904,0.00004478813,0.9429235,0.00004463884,0.00004721561,0.00006052531,0.001596879,0.002730315,0.0009264976,0.00002553807,0.00009685101,0.05142003],"study_design_scores_gemma":[0.004899275,0.004298591,0.8894782,0.00373572,0.0005756015,0.006518616,0.03290746,0.03982214,0.007749593,0.003404223,0.005253035,0.001357538],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968615,0.0007883678,0.000380021,0.001201546,0.0004772556,0.0001702232,0.0000068879,0.0000397595,0.00007445348],"genre_scores_gemma":[0.9944241,0.0001445602,0.005232583,0.00001086137,0.0001624488,0.000001366717,3.48148e-7,0.00001242743,0.0000113057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05344532,"threshold_uncertainty_score":0.8408805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01981184316329753,"score_gpt":0.2585729054485037,"score_spread":0.2387610622852062,"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."}}