{"id":"W2049356376","doi":"10.1155/2009/167239","title":"Forecasts of Tropical Pacific Sea Surface Temperatures by Neural Networks and Support Vector Regression","year":2009,"lang":"en","type":"article","venue":"International Journal of Oceanography","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Sea surface temperature; Support vector machine; Artificial neural network; Outlier; Principal component analysis; Linear regression; Regression; Algorithm; Artificial intelligence; Computer science; Geology; Mathematics; Climatology; Machine learning; Statistics","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":[0.001015882,0.0006334631,0.0004202416,0.0004744111,0.0001386003,0.0005555501,0.0004250906,0.0004116764,0.0007268172],"category_scores_gemma":[0.004773992,0.0002714078,0.0003826099,0.0006170225,0.0002260644,0.0006964483,0.000315051,0.0007450195,0.0001920912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007348935,"about_ca_system_score_gemma":0.0007584543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03609367,"about_ca_topic_score_gemma":0.02335996,"domain_scores_codex":[0.9996978,0.0001213243,0.0000219772,0.0000544475,0.00007376962,0.00003076055],"domain_scores_gemma":[0.9989115,0.0006049047,0.0002020387,0.00004106214,0.0002102954,0.00003022113],"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":[0.00005834009,0.00001864453,0.002468472,0.00002599129,0.0000168757,0.00001442532,0.00001195665,0.9750758,0.000522366,0.000737454,0.0003931982,0.02065651],"study_design_scores_gemma":[0.000003117551,0.000003944071,0.0003925524,0.000001480223,0.000001143718,6.454361e-7,0.000001805533,0.9991729,0.0000924639,0.0002890507,0.00003903115,0.000001902055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6253465,0.001153992,0.364749,0.000759613,0.0001231522,0.00006830345,0.001185348,0.0009561866,0.005657877],"genre_scores_gemma":[0.9517933,0.0003247243,0.04548715,0.00003181219,0.00004559164,0.00005633113,0.0006086296,0.00003055238,0.001621981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03609367,"threshold_uncertainty_score":0.07176721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006167890557689708,"score_gpt":0.219274841081855,"score_spread":0.2131069505241653,"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."}}