{"id":"W4401906365","doi":"10.1007/s11051-024-06111-2","title":"From ashes to answers: decoding acoustically agglomerated soot particle signatures","year":2024,"lang":"en","type":"article","venue":"Journal of Nanoparticle Research","topic":"Aerosol Filtration and Electrostatic Precipitation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; National Research Council Canada","funders":"National Research Council Canada","keywords":"Materials science; Soot; Particle (ecology); Decoding methods; Nanotechnology; Chemical engineering; Combustion; Telecommunications; Computer science; Physical chemistry","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":[0.0003427047,0.0006790902,0.0003384029,0.0004911831,0.0001828955,0.0006753207,0.0004474338,0.000681263,0.002515994],"category_scores_gemma":[0.002176227,0.0001437641,0.0003126003,0.0003174673,0.0002630559,0.0006661323,0.0005540229,0.0006344574,0.001353177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003030595,"about_ca_system_score_gemma":0.0006681611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0037162,"about_ca_topic_score_gemma":0.005392393,"domain_scores_codex":[0.9998241,0.00002958147,0.000006118079,0.00005654993,0.00003960931,0.00004417313],"domain_scores_gemma":[0.9996015,0.0001898495,0.00003116571,0.00003248085,0.0001153945,0.00002954669],"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":[0.001581317,0.0003649879,0.01774216,0.0002296691,0.0001223272,0.0003989487,0.0004996244,0.07493287,0.1384761,0.003438363,0.007308267,0.7549053],"study_design_scores_gemma":[0.00001869287,0.0001363894,0.007660739,0.00003037506,0.00003565858,0.00008157761,0.0002557428,0.9477414,0.03670808,0.004224699,0.003081849,0.00002476569],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6625686,0.0008918176,0.3209909,0.001169873,0.0005761339,0.000167003,0.00182255,0.002590116,0.009223036],"genre_scores_gemma":[0.9440399,0.0002437997,0.04855153,0.0002493321,0.00008314468,0.0000488119,0.001078144,0.00007926431,0.005626165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0037162,"threshold_uncertainty_score":0.008416891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04628508612667961,"score_gpt":0.3619048752450128,"score_spread":0.3156197891183332,"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."}}