{"id":"W2051571600","doi":"10.1175/jas3544.1","title":"Estimating Cloud Optical Depth from Surface Radiometric Observations: Sensitivity to Instrument Noise and Aerosol Contamination","year":2005,"lang":"en","type":"article","venue":"Journal of the Atmospheric Sciences","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Canadian Space Agency; Canadian Foundation for Climate and Atmospheric Sciences; Canadian Meteorological and Oceanographic Society; Université du Québec à Montréal","keywords":"Aerosol; Radiometry; Remote sensing; Environmental science; Albedo (alchemy); Noise (video); Monte Carlo method; Cloud base; Optical depth; Effective radius; Satellite; Wavelength; Optics; Cloud computing; Meteorology; Physics; Geology; Computer science; Statistics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002322531,0.0002993231,0.0003204165,0.0006125879,0.0002259664,0.0005549492,0.0003975245,0.0003670412,0.0002210131],"category_scores_gemma":[0.01704101,0.000232701,0.0002234266,0.0005186502,0.00030469,0.0005029843,0.000374353,0.0002642978,0.0001041742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003655719,"about_ca_system_score_gemma":0.000269437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005832737,"about_ca_topic_score_gemma":0.003484485,"domain_scores_codex":[0.9991391,0.0002888139,0.00005978125,0.0001460436,0.0003065265,0.00005969306],"domain_scores_gemma":[0.9885598,0.009072217,0.0007031363,0.0009086655,0.0006564097,0.00009977145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001478006,0.0002063434,0.4055408,0.0002544422,0.0004466135,0.0002747294,0.0002726945,0.3338636,0.10906,0.001168582,0.0003390248,0.1470951],"study_design_scores_gemma":[0.00004260405,0.000272882,0.1778112,0.00003187009,0.0000949319,0.0003410989,0.00007861437,0.7343316,0.08521539,0.001413715,0.0003129529,0.00005310024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9014927,0.0001880105,0.09747289,0.00003830189,0.00001369605,0.00003413759,0.0001607651,0.000248482,0.0003509719],"genre_scores_gemma":[0.980256,0.00004859852,0.01944428,0.00001704492,0.000004737213,0.00001179921,0.0001262706,0.00001487227,0.00007640911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005832737,"threshold_uncertainty_score":0.01228285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02184091288043881,"score_gpt":0.2458231173467898,"score_spread":0.223982204466351,"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."}}