{"id":"W2549148406","doi":"","title":"Autonomous Ozone and Aerosol Lidar Platform: Preliminary Results","year":2014,"lang":"en","type":"article","venue":"2014 AGU Fall Meeting","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Aerosol; Lidar; Environmental science; Remote sensing; Meteorology; Ozone; Atmospheric sciences; Geography; Geology","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":[0.002410505,0.001215244,0.001097549,0.0004561511,0.001219961,0.001514249,0.001682113,0.00201362,0.003217972],"category_scores_gemma":[0.001058409,0.000390521,0.0009302776,0.0006243057,0.0007510418,0.001453891,0.001406544,0.001281395,0.00181524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009102512,"about_ca_system_score_gemma":0.001936237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01970958,"about_ca_topic_score_gemma":0.03463334,"domain_scores_codex":[0.9986138,0.0001561796,0.00002719404,0.0002230286,0.0005991042,0.0003806319],"domain_scores_gemma":[0.9986094,0.0001661054,0.00007365242,0.0002076726,0.0005312014,0.0004118554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.008889427,0.011075,0.1543436,0.001035809,0.002381336,0.00203305,0.001946764,0.05800575,0.5646915,0.002428321,0.05129771,0.1418719],"study_design_scores_gemma":[0.00349893,0.008960955,0.5197341,0.0001332603,0.001519038,0.0007859835,0.00168189,0.2185505,0.1655826,0.003314502,0.07569198,0.0005462093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.938352,0.0005394489,0.01676697,0.001110966,0.0004606375,0.0009504387,0.02069547,0.003083115,0.01804093],"genre_scores_gemma":[0.9152331,0.0001943715,0.04719353,0.0008453928,0.00015323,0.0004085447,0.02885333,0.0004316074,0.006687008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01970958,"threshold_uncertainty_score":0.03918976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008893501557097842,"score_gpt":0.2084451673004677,"score_spread":0.1995516657433699,"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."}}