{"id":"W2071644800","doi":"10.1029/2004jc002595","title":"Hybrid coupled modeling of the tropical Pacific using neural networks","year":2005,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Climate variability and models","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nonlinear system; Principal component analysis; Sea surface temperature; Mode (computer interface); Artificial neural network; Atmospheric model; Climatology; Meteorology; Environmental science; Geology; Physics; Computer science; Artificial intelligence","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":[0.0005084889,0.0005899364,0.0004439997,0.0003781635,0.0003519239,0.0007310567,0.0006991393,0.0005394699,0.001050367],"category_scores_gemma":[0.0009992698,0.0004277413,0.0006016745,0.0004844296,0.0004525019,0.0009117767,0.0007581218,0.0004969875,0.000102392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008402013,"about_ca_system_score_gemma":0.0006074631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02755567,"about_ca_topic_score_gemma":0.01877439,"domain_scores_codex":[0.9998271,0.00006240215,0.00001015562,0.00004435284,0.00003136487,0.00002465831],"domain_scores_gemma":[0.9996933,0.0001717846,0.00003995465,0.00002122099,0.00005658585,0.00001717566],"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":[0.00001667373,0.000007691374,0.0003768413,0.000007104531,0.00002318097,0.00001319329,0.00001118238,0.9967586,0.0003325533,0.0006352342,0.00004301227,0.001774608],"study_design_scores_gemma":[0.000001727042,0.000002610087,0.00008442465,3.78884e-7,0.00000195063,7.459719e-7,0.000001130313,0.9994966,0.00004542041,0.0003365346,0.00002715171,0.000001289686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4690762,0.0005411488,0.5185894,0.0003101399,0.000078121,0.00007786937,0.000453338,0.0009868101,0.009887035],"genre_scores_gemma":[0.9793862,0.0001249668,0.0182658,0.00002767585,0.00001933416,0.00008389253,0.0001193795,0.00002921045,0.001943617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02755567,"threshold_uncertainty_score":0.05479056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05168702040011077,"score_gpt":0.3152622879792721,"score_spread":0.2635752675791614,"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."}}