{"id":"W1979425827","doi":"10.1007/s003820000119","title":"A neural network atmospheric model for hybrid coupled modelling","year":2001,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Climate variability and models","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wind stress; Climatology; Sea surface temperature; Environmental science; Atmosphere (unit); Pacific ocean; Atmospheric model; Ocean current; Ocean heat content; Wind speed; Geology; Atmospheric sciences; Oceanography; Meteorology; Geography","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":[0.0003425606,0.0003923377,0.000565657,0.0002656881,0.0005898204,0.0006550527,0.001263721,0.00113372,0.003124486],"category_scores_gemma":[0.001309511,0.0004192948,0.0004550809,0.0005407056,0.0003598279,0.001127593,0.0009570207,0.00109269,0.0005842383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005707173,"about_ca_system_score_gemma":0.0007119888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02389824,"about_ca_topic_score_gemma":0.02063617,"domain_scores_codex":[0.9998904,0.00002893178,0.000007505087,0.00003114565,0.00002851568,0.00001344793],"domain_scores_gemma":[0.9997626,0.0001106088,0.00001950825,0.00002642082,0.00006169739,0.00001907614],"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":[0.00003194317,0.00001702794,0.0002596328,0.00001513844,0.00002917785,0.00001842182,0.00001027914,0.9834275,0.000569224,0.003114558,0.0006219884,0.01188517],"study_design_scores_gemma":[0.00000388167,0.00000240816,0.00004552665,6.599742e-7,0.000003557411,0.000001549462,5.005534e-7,0.9987713,0.0000540161,0.0008954732,0.0002192828,0.000001938662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08272018,0.0006788296,0.8999414,0.000631178,0.0004148952,0.00007765551,0.001183501,0.002041531,0.01231087],"genre_scores_gemma":[0.8233709,0.0004986569,0.1627295,0.0001596939,0.0002219612,0.0002531417,0.0008395523,0.0002750638,0.01165152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02389824,"threshold_uncertainty_score":0.04751825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02301267139951356,"score_gpt":0.2359938798663,"score_spread":0.2129812084667865,"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."}}