{"id":"W4353093529","doi":"10.1080/01431161.2023.2187723","title":"A Bayesian neural network approach for tropospheric temperature retrievals from a lidar instrument","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; National Research Council Canada","funders":"","keywords":"Lidar; Artificial neural network; Altitude (triangle); Environmental science; Remote sensing; Troposphere; Range (aeronautics); Atmospheric temperature; Meteorology; Bayesian probability; Temperature measurement; Computer science; Mathematics; Artificial intelligence; Geology; Physics; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002692433,0.000159397,0.0002099752,0.00001345043,0.00009771335,0.00007589778,0.0003082229,0.00008909516,0.00007590095],"category_scores_gemma":[0.00005177718,0.0001388293,0.000182712,0.0002325721,0.00008990599,0.0001957509,0.0001508688,0.0002255645,0.00001396387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003696689,"about_ca_system_score_gemma":0.00001377633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001358073,"about_ca_topic_score_gemma":0.000007427816,"domain_scores_codex":[0.9984632,0.00004462049,0.0004044859,0.0002113645,0.0006153288,0.0002610594],"domain_scores_gemma":[0.9993576,0.00006530648,0.0003170052,0.0001204644,0.0000260646,0.0001135323],"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":[0.0002739261,0.00002653634,0.003457546,0.000002862325,0.0001472246,0.0001213534,0.000332625,0.6779304,0.005981692,0.000003999371,0.001492582,0.3102292],"study_design_scores_gemma":[0.0008310381,0.0001309662,0.01021827,0.000050214,0.00003998533,0.0002091694,0.0003864394,0.9814387,0.0002463709,0.001920326,0.00432837,0.0002001715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.728494,0.00004102997,0.2694009,0.0006571675,0.0009039998,0.0001235866,0.000004812669,0.00002473105,0.0003497812],"genre_scores_gemma":[0.6167216,0.00006133422,0.3820059,0.0003677552,0.000572706,6.676196e-8,0.0000166137,0.0000234529,0.0002304979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.310029,"threshold_uncertainty_score":0.5661296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009442513360958683,"score_gpt":0.2261024453105703,"score_spread":0.2166599319496116,"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."}}