{"id":"W3029390389","doi":"10.1007/978-3-030-46133-1_36","title":"Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Combustion and flame dynamics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Representation (politics); Manifold (fluid mechanics); Artificial neural network; Combustion; Artificial intelligence; Deep neural networks; Computer science; Pattern recognition (psychology); Mathematics; Topology (electrical circuits); Chemistry; Political science; Engineering; Mechanical engineering; Combinatorics; Physical chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000121064,0.0002767948,0.0003633999,0.0002938564,0.00006891471,0.00006063092,0.0003399873,0.0001878776,0.00002060197],"category_scores_gemma":[0.00002494132,0.0002907185,0.0000993881,0.0003277081,0.000172069,0.0001412117,0.0001276423,0.0005495723,0.000002819503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001777906,"about_ca_system_score_gemma":0.00003241075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001584085,"about_ca_topic_score_gemma":0.00004015007,"domain_scores_codex":[0.9985808,0.0000159352,0.000383766,0.000412927,0.0003744874,0.0002320469],"domain_scores_gemma":[0.9992362,0.0001445736,0.0001243101,0.0002972455,0.000105614,0.00009203589],"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":[0.000007292106,0.000007384988,0.0001012302,0.00003203842,0.000009621534,0.00001684014,0.00009074828,0.9613311,0.0003611858,0.0003090535,0.000003233254,0.03773025],"study_design_scores_gemma":[0.0002331655,0.00003034166,0.0003792577,0.0001275183,0.00001601798,0.00003034976,2.04938e-7,0.9978032,0.000154884,0.000961952,0.000005867445,0.0002572408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001981793,0.0002304449,0.9960999,0.00004742825,0.001207874,0.0002066937,0.000004871323,0.0001151077,0.0001058662],"genre_scores_gemma":[0.9581691,0.00001199427,0.04140826,0.0001538212,0.0001795319,5.80255e-7,0.00003177248,0.00003668792,0.000008259271],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9561873,"threshold_uncertainty_score":0.9999545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02571370220045699,"score_gpt":0.2507259828105365,"score_spread":0.2250122806100796,"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."}}