{"id":"W4313418403","doi":"10.1080/13647830.2022.2153740","title":"Application of machine learning in low-order manifold representation of chemistry in turbulent flames","year":2022,"lang":"en","type":"article","venue":"Combustion Theory and Modelling","topic":"Combustion and flame dynamics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canada Foundation for Innovation; Government of Ontario","keywords":"Curse of dimensionality; Representation (politics); Artificial neural network; Computational fluid dynamics; Computer science; Turbulence; Manifold (fluid mechanics); Artificial intelligence; Algorithm; Machine learning; Chemistry; Applied mathematics; Mathematics; Thermodynamics; Mechanical engineering; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003894352,0.00006606222,0.0001241207,0.0001004575,0.00003015095,0.000003265423,0.00005374029,0.00003247082,0.00003252125],"category_scores_gemma":[0.00001369455,0.00008187701,0.00002032551,0.0002371015,0.00001562983,0.00004639067,0.00002981919,0.0002099321,2.16566e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003971716,"about_ca_system_score_gemma":0.000006534076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001954896,"about_ca_topic_score_gemma":0.000003321937,"domain_scores_codex":[0.9994139,0.0000670227,0.0002534809,0.0001084529,0.00008399245,0.00007312066],"domain_scores_gemma":[0.999741,0.00007302754,0.00005857657,0.00009164197,0.00002128282,0.00001450801],"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":[0.00006251656,0.00003519746,0.001272705,0.0001461007,0.000004312624,7.995139e-7,0.0006296185,0.9842557,0.005459787,0.005188343,5.237037e-7,0.00294443],"study_design_scores_gemma":[0.0003340923,0.000008276828,0.000175112,0.00003317996,0.000005373582,0.000002869495,0.0005430166,0.9912297,0.003707704,0.003875063,0.00001538705,0.00007028399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5926263,0.000294004,0.4068272,0.000009747905,0.0000189133,0.00007512699,0.000002276872,0.00002467813,0.0001217181],"genre_scores_gemma":[0.9993715,0.0002220517,0.0002450903,0.000004417571,0.000005375751,0.00002200746,0.00005528946,0.00001072519,0.00006356098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4067451,"threshold_uncertainty_score":0.3338847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00766151676738656,"score_gpt":0.2105217048355324,"score_spread":0.2028601880681459,"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."}}