{"id":"W2006632683","doi":"10.1117/12.686010","title":"Bioaerosols laser-induced fluorescence provides specific robust signatures for standoff detection","year":2006,"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":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Indoor bioaerosol; Bioaerosol; Spectral signature; Fluorescence; Robustness (evolution); Biological warfare; Remote sensing; Laser-induced fluorescence; Lidar; Laser; Environmental science; Materials science; Biological system; Computer science; Optics; Chemistry; Physics; Environmental chemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002664332,0.0003347333,0.0002401796,0.0004384799,0.0001816324,0.0003999734,0.0003441625,0.0004928403,0.0004120539],"category_scores_gemma":[0.0004078407,0.0002156095,0.0001890942,0.0003077871,0.0003665604,0.0003886811,0.0004379711,0.000455399,0.0002623026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000303011,"about_ca_system_score_gemma":0.0002323846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001147144,"about_ca_topic_score_gemma":0.002718272,"domain_scores_codex":[0.9996773,0.00002789027,0.00001272238,0.00009217436,0.0001422718,0.00004764326],"domain_scores_gemma":[0.9997435,0.00007481243,0.00006214644,0.00003291218,0.00006578511,0.00002083742],"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":[0.00003752056,0.000008892677,0.001292178,0.00001812729,0.000003540198,0.00001657974,0.00002246122,0.0001549749,0.9938023,0.00006237836,0.00003375361,0.004547121],"study_design_scores_gemma":[0.000003338916,0.00005904974,0.007086456,0.000005263643,0.000005591099,0.00009812098,0.00004096502,0.002628548,0.989101,0.00007382112,0.0008862797,0.00001166798],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9619294,0.000645971,0.03494537,0.00009347643,0.0000172648,0.00002542154,0.0002618488,0.0005200071,0.001561278],"genre_scores_gemma":[0.973093,0.0004045554,0.02515221,0.00006417034,0.000005975247,0.00002952149,0.0003247815,0.00004384283,0.0008817918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001147144,"threshold_uncertainty_score":0.002280951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179601870084964,"score_gpt":0.2225525250338049,"score_spread":0.2045923380253085,"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."}}