{"id":"W2729319959","doi":"","title":"CLOUD AND AEROSOL PROPERTIES MEASURED WITH A LIDAR IN THE HIGH ARCTIC AT EUREKA","year":2010,"lang":"en","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Aerosol; Lidar; Arctic; Environmental science; The arctic; Meteorology; Cloud computing; Remote sensing; Climatology; Atmospheric sciences; Geography; Geology; Oceanography; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002150921,0.0001149792,0.0001132444,0.00000593922,0.0001870335,0.00001528536,0.0001747389,0.00001619006,0.0002511161],"category_scores_gemma":[0.000002098236,0.00006918895,0.000008124053,0.00006737452,0.0002110339,0.0001457355,0.00007758407,0.0001485578,5.681985e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000450361,"about_ca_system_score_gemma":0.0001493518,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01575771,"about_ca_topic_score_gemma":0.2926759,"domain_scores_codex":[0.9984795,0.00003258885,0.0001328503,0.0001874109,0.0009629056,0.0002046813],"domain_scores_gemma":[0.9996075,0.00005755543,0.00004439979,0.0001594541,1.28192e-7,0.0001309717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006995862,0.00009112577,0.8396498,0.000125142,0.00001739531,0.0001401208,0.0007752469,0.0001519965,0.1493337,0.005569049,0.0004407868,0.003006049],"study_design_scores_gemma":[0.0007080607,0.0002016059,0.9155349,0.0001154014,0.00002396593,0.00005519762,0.0016401,0.0007417945,0.05825086,0.000316046,0.02203139,0.0003806769],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9492804,0.00004824304,0.000002876885,0.003591871,0.00004081584,0.0001550728,0.00001911356,0.000003231813,0.04685833],"genre_scores_gemma":[0.9966391,0.0000247259,0.0002319397,0.0006216889,0.00001371806,0.000008691045,0.000001204767,0.00000773129,0.002451159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2769182,"threshold_uncertainty_score":0.9907964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002843928186382489,"score_gpt":0.1180172772825596,"score_spread":0.1151733490961771,"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."}}