{"id":"W2001220004","doi":"10.1364/ors.2003.owa5","title":"NIR Spectral Investigations of Water in Clouds with FTS","year":2003,"lang":"en","type":"article","venue":"","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"","keywords":"Remote sensing; Liquid water; Environmental science; Water ice; Liquid water content; Aerospace engineering; Astrobiology; Materials science; Cloud computing; Geology; Physics; Earth science; Computer science; Engineering","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":[0.00007782641,0.0002455481,0.0001635168,0.0005201951,0.0003728944,0.0002153041,0.0001982505,0.0001711151,0.001368836],"category_scores_gemma":[0.0001734136,0.0001034011,0.0001160722,0.0007505103,0.0001356846,0.0003054971,0.0002098575,0.0001683552,0.0002099712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002023989,"about_ca_system_score_gemma":0.0001133646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004378637,"about_ca_topic_score_gemma":0.004689047,"domain_scores_codex":[0.9999175,0.000004336625,0.000002159566,0.00001195167,0.00004398522,0.00002010083],"domain_scores_gemma":[0.9999163,0.00001716892,0.00001880031,0.000006849346,0.00002990876,0.00001086465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004458913,0.000112096,0.04347419,0.0001442233,0.00004729185,0.000282985,0.0002246163,0.002792361,0.9138811,0.0004524651,0.0009592235,0.03718347],"study_design_scores_gemma":[0.00002009754,0.000416212,0.2978149,0.00003053499,0.00007752561,0.0008876315,0.0004210973,0.0260799,0.6643012,0.0006618769,0.009249831,0.00003915726],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905756,0.0003548447,0.003040256,0.00004097963,0.00001447032,0.00000869956,0.0008764808,0.0001052075,0.004983309],"genre_scores_gemma":[0.9935622,0.0002993795,0.003994141,0.0000230166,0.00001572288,0.000005693792,0.0007787711,0.00002399999,0.001296988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004378637,"threshold_uncertainty_score":0.008706331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00682049199784552,"score_gpt":0.1894403619928623,"score_spread":0.1826198699950168,"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."}}