{"id":"W1996950161","doi":"10.1016/j.optcom.2007.05.044","title":"Multi-constituents detection in contaminated aerosol clouds using remote-filament-induced breakdown spectroscopy","year":2007,"lang":"en","type":"article","venue":"Optics Communications","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bombardier Recreational Products (Canada); Université Laval; Institut National d'Optique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Protein filament; Spectroscopy; Materials science; Aqueous solution; Aerosol; Excited state; Analytical Chemistry (journal); Spectral line; Ion; Optics; Atomic physics; Chemistry; Environmental chemistry; Physics; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002211419,0.000253315,0.0002405697,0.0006488721,0.0004924543,0.0004568084,0.0005975623,0.0006901113,0.0006888266],"category_scores_gemma":[0.0003878014,0.0002072466,0.0001847229,0.000293275,0.0002864122,0.0005690628,0.0003476681,0.0004389659,0.0001305209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003535993,"about_ca_system_score_gemma":0.0001513594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001423432,"about_ca_topic_score_gemma":0.00194996,"domain_scores_codex":[0.9997827,0.00003412051,0.000006730661,0.00005763469,0.0000790051,0.00003979197],"domain_scores_gemma":[0.999763,0.00008153338,0.00005038411,0.00001762933,0.000059566,0.00002794279],"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":[0.0006668114,0.0001433704,0.02195781,0.00005957242,0.00006321428,0.0003280363,0.0001088746,0.001626866,0.964142,0.0003040592,0.0002123048,0.01038705],"study_design_scores_gemma":[0.00009532575,0.0004337169,0.09799045,0.00001675306,0.00009824699,0.001280746,0.0002644061,0.05230994,0.8458768,0.0005291188,0.001063601,0.00004101791],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903101,0.0005350752,0.00775149,0.00006560185,0.00001927417,0.00001446059,0.00009303136,0.00009956736,0.001111466],"genre_scores_gemma":[0.996085,0.00009033373,0.003428404,0.00002736197,0.00001096694,0.000005382709,0.00008224093,0.000007686836,0.0002626728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001423432,"threshold_uncertainty_score":0.002830267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03992737266197822,"score_gpt":0.3000630577847904,"score_spread":0.2601356851228122,"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."}}