{"id":"W2164088666","doi":"10.1080/02786826.2011.650334","title":"Strategies to Enhance the Interpretation of Single-Particle Ambient Aerosol Data","year":2012,"lang":"en","type":"article","venue":"Aerosol Science and Technology","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Aerosol; Particle (ecology); Particulates; Cluster analysis; Interpretation (philosophy); Data mining; Environmental science; Mass spectrometry; Computer science; Meteorology; Chemistry; Artificial intelligence; Geography; Geology","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":[0.007291476,0.001310153,0.0007936973,0.006227938,0.0008132356,0.002897741,0.001839756,0.001032147,0.001913608],"category_scores_gemma":[0.014253,0.0005324559,0.001174864,0.002293725,0.0007553369,0.002167031,0.001853803,0.00124254,0.001355417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004597371,"about_ca_system_score_gemma":0.0009442304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00178895,"about_ca_topic_score_gemma":0.004317485,"domain_scores_codex":[0.9973516,0.001067516,0.0002273986,0.0005140915,0.0007621793,0.00007706185],"domain_scores_gemma":[0.9907323,0.004562725,0.0007568208,0.001344257,0.002446097,0.0001579238],"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":[0.0004564089,0.0004330933,0.02270035,0.001088739,0.0005651693,0.0009203409,0.002750952,0.02754429,0.1846488,0.008521196,0.00408518,0.7462854],"study_design_scores_gemma":[0.0001726626,0.0005313765,0.06302337,0.0003809214,0.0005751035,0.00154679,0.002639238,0.6019738,0.2219272,0.03851755,0.06824757,0.0004643583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03905008,0.000366514,0.9554237,0.0003064326,0.00007845888,0.0002157193,0.0004180868,0.002756157,0.00138498],"genre_scores_gemma":[0.05014822,0.0001395527,0.9486673,0.00005391144,0.00005370699,0.0001042089,0.0003263798,0.0002571929,0.0002494952],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007291476,"threshold_uncertainty_score":0.03856152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01929596488156175,"score_gpt":0.2628214162981619,"score_spread":0.2435254514166001,"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."}}