{"id":"W2767970493","doi":"10.5194/amt-10-4253-2017","title":"Depolarization calibration and measurements using the CANDAC Rayleigh–Mie–Raman lidar at Eureka, Canada","year":2017,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Dalhousie University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Indigenous and Northern Affairs Canada; Environment and Climate Change Canada; Eurostars; Strong; Ontario Innovation Trust","keywords":"Lidar; Depolarization ratio; Remote sensing; Environmental science; Calibration; Ice cloud; Rayleigh scattering; Depolarization; Optics; Physics; Geology; Radiative transfer","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0009078888,0.000352108,0.0002560701,0.0006980644,0.002572994,0.0007851925,0.001029664,0.0003794913,0.0006693109],"category_scores_gemma":[0.0007883346,0.000216343,0.0002269367,0.001003563,0.0005137924,0.0005112691,0.0006484777,0.0008908933,0.0002775294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009468882,"about_ca_system_score_gemma":0.0113175,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8260573,"about_ca_topic_score_gemma":0.9068235,"domain_scores_codex":[0.998744,0.00004395271,0.00001686202,0.000172484,0.0008742754,0.0001484069],"domain_scores_gemma":[0.998866,0.00003056082,0.00003077925,0.00002891724,0.0009852042,0.00005866767],"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":[0.001319511,0.0009039229,0.2953591,0.0002816552,0.0002091744,0.0006525737,0.001984759,0.03557652,0.479575,0.002733397,0.009224134,0.1721802],"study_design_scores_gemma":[0.0002339962,0.0005512174,0.5998856,0.00008290665,0.0001142951,0.0003399934,0.002043993,0.09025187,0.2816579,0.0005232087,0.02405203,0.0002629414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768123,0.0003368256,0.00690794,0.0002872834,0.00004338784,0.0001339295,0.001854453,0.0003189149,0.01330492],"genre_scores_gemma":[0.9874246,0.0001499536,0.009154243,0.00008691497,0.000004447922,0.00003005985,0.000789366,0.00003557383,0.002324878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1739427,"threshold_uncertainty_score":0.3499342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03523181252701652,"score_gpt":0.2357692126301827,"score_spread":0.2005374001031662,"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."}}