{"id":"W2896233414","doi":"10.1109/group4.2018.8478722","title":"Automating Photonic Design with Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Solver; Computer science; Artificial neural network; MATLAB; Nonlinear system; Photonics; Parametric statistics; Finite-difference time-domain method; Grating; Range (aeronautics); Electronic engineering; Algorithm; Computational science; Artificial intelligence; Engineering; Optics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002859028,0.0001036315,0.00009838096,0.00004149648,0.0003492855,0.0001849261,0.000515277,0.00002453009,0.0000428725],"category_scores_gemma":[0.00001402345,0.00006494844,0.00002280947,0.0003616112,0.00004006829,0.0002218862,0.0002600998,0.0001601225,0.0000603596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001262379,"about_ca_system_score_gemma":0.00003007035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003272693,"about_ca_topic_score_gemma":0.00001286679,"domain_scores_codex":[0.9990521,0.00008271002,0.0001197762,0.000272561,0.0001721074,0.0003007234],"domain_scores_gemma":[0.9994543,0.0001123533,0.00006325706,0.0002464845,0.00005738595,0.00006624043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007626091,0.0001921296,0.02019719,0.00007072648,0.0001662577,0.0003100432,0.002916101,0.2232578,0.01517153,0.03583582,0.003127758,0.6986784],"study_design_scores_gemma":[0.00015031,0.0003203751,0.0003390797,0.00002952055,0.000001422935,0.00004707546,0.000004511588,0.993592,0.004547628,0.000156531,0.0006923968,0.0001192026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03404152,0.00005581155,0.9598981,0.0002877812,0.0001130688,0.00009102713,1.821992e-8,0.0005753606,0.004937291],"genre_scores_gemma":[0.6948929,0.000001982064,0.3041915,0.0001973988,0.00009842034,0.000001834403,1.585285e-7,0.000006827902,0.0006089612],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7703342,"threshold_uncertainty_score":0.2686457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01832202539061403,"score_gpt":0.2262440749081126,"score_spread":0.2079220495174986,"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."}}