{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003969221,0.0002223159,0.000247938,0.000004757948,0.0003236626,0.00005566969,0.0002790988,0.00008195992,0.0002088604],"category_scores_gemma":[0.00001462221,0.0002242124,0.0001418054,0.0001775317,0.0001171209,0.0002487701,0.0002231185,0.0001311301,0.00007101418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003041503,"about_ca_system_score_gemma":0.00001004078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009226911,"about_ca_topic_score_gemma":0.000246444,"domain_scores_codex":[0.9981923,0.00002052967,0.0003421992,0.0004714105,0.0001851331,0.0007884427],"domain_scores_gemma":[0.9992505,0.00009753869,0.0001004138,0.0003965271,0.00001570005,0.0001393451],"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":[0.00008944369,0.00007138259,0.005135394,0.00001851674,0.00000566655,0.000003808478,0.0000731838,0.9919428,0.00002956854,0.001577919,0.000128302,0.0009240673],"study_design_scores_gemma":[0.0003962021,0.00003984978,0.00007266353,0.000009010751,0.00003168642,0.00001485666,0.00002113043,0.9860039,6.331539e-7,0.01292281,0.0002202543,0.0002669957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5044717,0.0000131283,0.4935187,0.0001152368,0.0001195739,0.0002969792,0.00004524348,0.00008882775,0.001330559],"genre_scores_gemma":[0.9495519,0.0002968364,0.04898296,0.0004171064,0.00006601663,0.00008512848,0.0001510774,0.00005161665,0.0003973156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4450802,"threshold_uncertainty_score":0.9143118,"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."}}