{"id":"W4296751870","doi":"10.5194/egusphere-2022-802-ac1","title":"Reply on CEC1","year":2022,"lang":"en","type":"peer-review","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Austrian Science Fund; European Commission; University of Victoria","keywords":"Pathfinder; Simplicity; Computer science; Climate model; Simple (philosophy); Data assimilation; Calibration; Inference; Range (aeronautics); Bayesian inference; Meteorology; Bayesian probability; Artificial intelligence; Climate change; Geography; Engineering; Mathematics; Geology; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004942094,0.0009922709,0.001109657,0.001317155,0.004193557,0.004841479,0.003208782,0.03562075,0.07627087],"category_scores_gemma":[0.03834644,0.0006898278,0.001396877,0.001207419,0.00268239,0.005235639,0.002647583,0.02711904,0.05895076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004489228,"about_ca_system_score_gemma":0.005728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008965456,"about_ca_topic_score_gemma":0.01314875,"domain_scores_codex":[0.9962717,0.0006183728,0.0003444049,0.0006049246,0.001610001,0.0005506325],"domain_scores_gemma":[0.9862618,0.004560184,0.000523007,0.0006404474,0.006215877,0.001798816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007366551,0.000002641836,0.00002928224,0.00001133091,0.000001076986,0.00003256685,0.000008231339,0.000003296993,0.00001514865,0.0004003065,0.9983303,0.001158512],"study_design_scores_gemma":[0.00001330736,0.000007907125,0.0002973949,0.00005665005,0.000003103017,0.00006281794,0.00005368453,0.00002638319,0.00006592204,0.0007669256,0.9986318,0.00001407595],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001433068,0.001012768,0.0001340132,0.9023446,0.08376795,0.00004989506,0.0004237217,0.000180847,0.0119429],"genre_scores_gemma":[0.001337181,0.0004975749,0.0001329723,0.9356703,0.02484921,0.00008340106,0.0001538441,0.00009868594,0.03717672],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.07627087,"threshold_uncertainty_score":0.2551515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299231782232511,"score_gpt":0.2385055859135596,"score_spread":0.2255132680912345,"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."}}