{"id":"W3110416003","doi":"10.1364/fio.2020.fth2c.5","title":"Identifying Optimal Photonic Crystal Sensor Designs with Machine Learning","year":2020,"lang":"en","type":"article","venue":"Frontiers in Optics / Laser Science","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Laser linewidth; Photonic crystal; Sensitivity (control systems); Artificial neural network; Slab; Optics; Wavelength; Computer science; Photonics; Optoelectronics; Materials science; Electronic engineering; Artificial intelligence; Physics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007284311,0.0006857504,0.0005449929,0.0005049165,0.0002162847,0.0005079226,0.0004944159,0.0008390885,0.0009080691],"category_scores_gemma":[0.002818062,0.00052518,0.0002754463,0.0003143984,0.000604618,0.0008700843,0.0003636095,0.0005551396,0.0002071488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000747812,"about_ca_system_score_gemma":0.001342957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001660538,"about_ca_topic_score_gemma":0.002764567,"domain_scores_codex":[0.9997529,0.00006586112,0.00001030924,0.00006331857,0.00007362524,0.00003391472],"domain_scores_gemma":[0.9991359,0.0004753586,0.0001387385,0.00005812787,0.0001673419,0.00002448025],"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":[0.00007432427,0.0001017842,0.0009281718,0.00008152134,0.00002993168,0.00002720343,0.00001634037,0.9426596,0.01533209,0.003930803,0.0006724598,0.03614577],"study_design_scores_gemma":[0.000003559022,0.00001367293,0.00003936775,0.000001823093,0.000002147162,0.000002230257,0.000001900813,0.9973671,0.001696424,0.0008105721,0.00005957419,0.000001801156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2777686,0.0005642764,0.7136573,0.0005875091,0.00004762494,0.0001081976,0.0001684722,0.0008706909,0.006227392],"genre_scores_gemma":[0.8120887,0.0001687818,0.1865033,0.0001098117,0.00001381685,0.0001137281,0.0001142782,0.00005191615,0.0008356015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001660538,"threshold_uncertainty_score":0.005425811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177188913616534,"score_gpt":0.221286970158714,"score_spread":0.2035680787970606,"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."}}