{"id":"W2385057993","doi":"10.1002/2015jd024543","title":"On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars","year":2016,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Biological and Environmental Research; McGill University","keywords":"Doppler effect; Doppler radar; Dissipation; Radar; Lidar; Cloud base; Boundary layer; Meteorology; Remote sensing; Environmental science; Physics; Mechanics; Geology; Cloud computing; Computer science","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":[0.001377224,0.00007980848,0.0001729268,0.00002042271,0.000200995,0.00004951731,0.0002035882,0.00004029345,0.0007240378],"category_scores_gemma":[0.001751915,0.00003437878,0.00005674206,0.0002612394,0.0003259718,0.0002413733,0.00001833584,0.0002315878,0.00001935912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001024523,"about_ca_system_score_gemma":0.00008071308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002772679,"about_ca_topic_score_gemma":0.00002610543,"domain_scores_codex":[0.9981843,0.0005546688,0.0003037239,0.000116536,0.0006184732,0.0002222528],"domain_scores_gemma":[0.9948823,0.004434712,0.0001885047,0.0001319456,0.0002239371,0.00013864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004972315,0.0005285209,0.06415462,0.0001059049,0.0002715358,0.0000659832,0.00106367,0.3196903,0.02676228,0.05812296,0.002750248,0.5215116],"study_design_scores_gemma":[0.0006804899,0.001792182,0.5830576,0.0001898258,0.00002121462,0.000006638671,0.0001605419,0.1658693,0.001025192,0.2468888,0.0001801893,0.0001280379],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963105,0.00009689142,0.001999795,0.001099404,0.00007576,0.0001153048,0.00001030676,0.000002925799,0.0002890973],"genre_scores_gemma":[0.9983503,0.00005463987,0.00135134,0.00003649893,0.0001203719,2.764514e-7,0.000001039605,0.000002335315,0.00008317228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5213836,"threshold_uncertainty_score":0.7927706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06340754983429872,"score_gpt":0.3180831094683682,"score_spread":0.2546755596340695,"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."}}