{"id":"W1988997592","doi":"10.1016/j.proci.2014.06.142","title":"Towards a predictive model for polycyclic aromatic hydrocarbon dimerization propensity","year":2014,"lang":"en","type":"article","venue":"Proceedings of the Combustion Institute","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Chemical Sciences, Geosciences, and Biosciences Division; Natural Sciences and Engineering Research Council of Canada; Office of Science; U.S. Department of Energy","keywords":"Nucleation; Molecular dynamics; Metadynamics; Soot; Monomer; Dimer; Chemistry; Chemical physics; Polycyclic aromatic hydrocarbon; Particle (ecology); Computational chemistry; Combustion; Organic chemistry; Polymer","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001664091,0.0001950921,0.0002627894,0.0001045118,0.0001317141,0.0000175167,0.0004575155,0.0001326348,6.367189e-7],"category_scores_gemma":[0.001337916,0.0001540399,0.0001024589,0.0003516915,0.0001623103,0.0004360247,0.0001611193,0.0002091173,0.000002082815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001052136,"about_ca_system_score_gemma":0.00002314177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000352032,"about_ca_topic_score_gemma":6.096465e-7,"domain_scores_codex":[0.9989443,0.000002832772,0.0003328789,0.0002550764,0.0002418089,0.0002230386],"domain_scores_gemma":[0.9993786,0.00002650543,0.0002657194,0.0001827779,0.0001024074,0.00004400404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001117546,0.00009240732,0.0006035473,0.0003218495,0.00006117304,3.442463e-8,0.0002708777,0.7842586,0.1435399,0.06679984,0.0002522299,0.003687815],"study_design_scores_gemma":[0.0004323419,0.00004524716,0.0004024101,0.0001477864,0.00005633078,0.000002718591,0.00001958554,0.8408856,0.1335242,0.024224,0.0001093356,0.000150404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2910902,0.000009152845,0.706027,0.0004631919,0.0002046013,0.0005319459,0.000004272735,0.0005202567,0.001149325],"genre_scores_gemma":[0.9829291,0.000006885525,0.01653205,0.00004605304,0.00005342637,0.0001501669,0.000006095813,0.00002933323,0.0002469654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6918388,"threshold_uncertainty_score":0.6281565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051125571376875,"score_gpt":0.2313237562831333,"score_spread":0.2108125005693646,"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."}}