{"id":"W4413760341","doi":"10.1039/d5ra03401a","title":"Novel AI technology for 4D <i>in situ</i> tracking and physicochemical characterization of inorganic carbonaceous aerosols in air/water","year":2025,"lang":"en","type":"article","venue":"RSC Advances","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"In situ; Characterization (materials science); Tracking (education); Environmental chemistry; Chemistry; Environmental science; Chemical engineering; Materials science; Nanotechnology; Organic chemistry; Engineering","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":[0.0003083392,0.0005188292,0.0002433143,0.0005104865,0.0002661785,0.000544658,0.0008535925,0.0008922075,0.003306135],"category_scores_gemma":[0.0004103387,0.0002903031,0.0002539415,0.0004146812,0.0004225884,0.0009858664,0.0007112669,0.000920001,0.001386867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005677381,"about_ca_system_score_gemma":0.0002689496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006064678,"about_ca_topic_score_gemma":0.001441693,"domain_scores_codex":[0.9997259,0.00002392687,0.00001333765,0.0000819471,0.000124669,0.00003024542],"domain_scores_gemma":[0.9997583,0.00008203137,0.00004830228,0.000038274,0.00005388046,0.00001927107],"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":[0.00002151774,0.00001188916,0.0001681543,0.00006399814,0.000004236439,0.00002734657,0.00002352831,0.0001720649,0.9911178,0.00077721,0.0004159023,0.007196349],"study_design_scores_gemma":[0.000005172601,0.00004995233,0.0005514362,0.000005657068,0.000007369662,0.0001021111,0.00002579111,0.007332511,0.9829434,0.0003323732,0.008625633,0.00001856768],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4732013,0.005239261,0.4745862,0.001574456,0.000829179,0.0003247909,0.00339984,0.004501904,0.03634299],"genre_scores_gemma":[0.6575388,0.00162567,0.324445,0.0009248701,0.0001524217,0.0004101353,0.00145126,0.0002094102,0.01324231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003306135,"threshold_uncertainty_score":0.01106006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005664882589556762,"score_gpt":0.2181138233816062,"score_spread":0.2124489407920494,"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."}}