{"id":"W2801198987","doi":"10.5194/amt-11-5531-2018","title":"Lidar temperature series in the middle atmosphere as a reference data set – Part 1: Improved retrievals and a 20-year cross-validation of two co-located French lidars","year":2018,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Université de Versailles Saint-Quentin-en-Yvelines; Centre National d’Etudes Spatiales; Institut national des sciences de l'Univers; Centre National de la Recherche Scientifique; European Commission","keywords":"Lidar; Remote sensing; Environmental science; Satellite; Initialization; Calibration; Atmosphere (unit); Ranging; Reference data; Altitude (triangle); Meteorology; Data set; Atmospheric temperature; Geology; Geodesy; Computer science; Geography; Mathematics; Physics; Statistics","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":[0.004726022,0.001115309,0.0008251156,0.001805939,0.0008684815,0.00134298,0.001095605,0.000959197,0.000664695],"category_scores_gemma":[0.003056459,0.0002896976,0.00151735,0.001660097,0.0004013561,0.001028871,0.0008179636,0.0006922724,0.0008508669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008600767,"about_ca_system_score_gemma":0.0006211672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03234455,"about_ca_topic_score_gemma":0.03435039,"domain_scores_codex":[0.9982505,0.0004421308,0.0001425087,0.0006631482,0.0003473801,0.0001542377],"domain_scores_gemma":[0.997521,0.0003213723,0.0002733406,0.0008645465,0.0009187354,0.0001009293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001651205,0.002269768,0.5428368,0.0003935951,0.002555111,0.0008963348,0.0007722292,0.1486528,0.08306483,0.0008371596,0.009448312,0.2066218],"study_design_scores_gemma":[0.0001460289,0.0004817909,0.7148278,0.00007857213,0.0004087622,0.0004115354,0.0002708848,0.2458539,0.02886282,0.0002066747,0.008330189,0.0001210753],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834689,0.0005813724,0.010339,0.00004258356,0.0000838487,0.00006664262,0.00395949,0.0007095563,0.0007485168],"genre_scores_gemma":[0.9584489,0.00009360963,0.01658574,0.00004062672,0.00003435633,0.00009532228,0.0241776,0.0001328564,0.0003908381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03234455,"threshold_uncertainty_score":0.06431258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07201445742671218,"score_gpt":0.2956342727196723,"score_spread":0.2236198152929601,"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."}}