{"id":"W2117691070","doi":"10.1002/fact.1018","title":"Multivariate data analysis of fluorescence signals from biological aerosols","year":2001,"lang":"en","type":"article","venue":"Field Analytical Chemistry & Technology","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Aerosol; Fluorescence; Environmental science; Multivariate statistics; Fluorescence spectrometry; Principal component analysis; Environmental chemistry; Analytical Chemistry (journal); Chromatography; Chemistry; Optics; Physics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001206464,0.0004965272,0.0004587314,0.001812582,0.0002644195,0.000578702,0.000299417,0.0002529756,0.0009828508],"category_scores_gemma":[0.003621357,0.00007898763,0.0005994713,0.001319682,0.000315498,0.0003801412,0.0003633236,0.0005464366,0.0002593021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002899966,"about_ca_system_score_gemma":0.0003487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001254155,"about_ca_topic_score_gemma":0.0007011413,"domain_scores_codex":[0.9991989,0.0001674939,0.0000548425,0.0001724709,0.000335694,0.00007073321],"domain_scores_gemma":[0.998466,0.000613649,0.0002628034,0.0001682641,0.0004161848,0.00007301728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009610691,0.0006390468,0.05479178,0.0005953734,0.0004313019,0.0004306726,0.000554898,0.02217063,0.3886582,0.003035154,0.002561565,0.5251703],"study_design_scores_gemma":[0.00007659483,0.001238262,0.3071494,0.00007917889,0.0003140558,0.001239207,0.0005705879,0.4124368,0.2551297,0.009221466,0.01226519,0.0002795858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6240472,0.0006186149,0.3689075,0.0002387883,0.00007557972,0.0001745729,0.002973126,0.001402125,0.00156248],"genre_scores_gemma":[0.8792142,0.0002988075,0.1168335,0.00003686802,0.00006324816,0.0001462247,0.002858986,0.0000723359,0.0004758158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001812582,"threshold_uncertainty_score":0.006380439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03942299446035351,"score_gpt":0.2684074843848772,"score_spread":0.2289844899245236,"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."}}