{"id":"W1991687785","doi":"10.1117/12.2028681","title":"Standoff detection of bioaerosols over wide area using a newly developed sensor combining a cloud mapper and a spectrometric LIF lidar","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Indoor bioaerosol; Lidar; Remote sensing; Environmental science; Cloud computing; Computer science; Meteorology; Geology; Physics","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":[0.0002415172,0.0003682281,0.0003425781,0.0003758029,0.0001960097,0.00038232,0.0006990727,0.0005685358,0.00043807],"category_scores_gemma":[0.0001815841,0.0001872983,0.0002245379,0.0002146078,0.0002049344,0.0005304901,0.0005076951,0.0003136696,0.0001792543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003174834,"about_ca_system_score_gemma":0.0003417869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001106152,"about_ca_topic_score_gemma":0.00206436,"domain_scores_codex":[0.9997315,0.00001908921,0.000007856966,0.00006792446,0.0001457934,0.00002781565],"domain_scores_gemma":[0.9998286,0.00003774981,0.0000339039,0.00001390325,0.00006449408,0.00002140614],"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":[0.0001063154,0.00005115298,0.003385792,0.00005461495,0.000008165002,0.00009504247,0.00005693655,0.001159905,0.969664,0.0001422653,0.0001981186,0.02507764],"study_design_scores_gemma":[0.00001982312,0.0004612473,0.009180954,0.00001093647,0.00001938118,0.0003172135,0.00007035458,0.0381256,0.9487582,0.00009935202,0.002907058,0.00002996221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9124898,0.0002757433,0.08375581,0.0001039532,0.00003942549,0.0001017965,0.0004759281,0.001105501,0.001652095],"genre_scores_gemma":[0.9093615,0.0001993471,0.0872676,0.0001261539,0.00001886579,0.00008732075,0.0003995078,0.00003568208,0.00250408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001106152,"threshold_uncertainty_score":0.002303541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01953967023941975,"score_gpt":0.2301347749490234,"score_spread":0.2105951047096037,"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."}}