{"id":"W2898720299","doi":"10.1038/s41467-019-12698-1","title":"Mapping the global design space of nanophotonic components using machine learning pattern recognition","year":2019,"lang":"en","type":"article","venue":"Nature Communications","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Nanophotonics; Curse of dimensionality; Set (abstract data type); Dimensionality reduction; Perspective (graphical); Pattern recognition (psychology); Feature (linguistics); Space (punctuation)","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":[0.0006239589,0.0009469593,0.0007674807,0.0007465474,0.0002381667,0.0009978494,0.0005621422,0.0006273602,0.001378919],"category_scores_gemma":[0.00165507,0.000426542,0.0006423809,0.0004266341,0.0008602807,0.001252091,0.0008144563,0.0008888782,0.0003665557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005904239,"about_ca_system_score_gemma":0.0004651346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004152279,"about_ca_topic_score_gemma":0.000643943,"domain_scores_codex":[0.9996318,0.0001031593,0.00002239846,0.00009106237,0.0001232587,0.00002839256],"domain_scores_gemma":[0.9992481,0.0003961589,0.00008636701,0.0001698559,0.00007773101,0.00002167502],"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":[0.00007329129,0.00008010036,0.001226862,0.0002638976,0.00006431036,0.00008448215,0.0000979613,0.8334652,0.04292858,0.01935966,0.0005939991,0.1017617],"study_design_scores_gemma":[0.000007295064,0.00007298505,0.000281979,0.00001220012,0.00000810689,0.00002969217,0.00002429799,0.9694471,0.01126288,0.0175044,0.001336106,0.00001301265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06889524,0.0003932883,0.9267565,0.0001869017,0.00001990707,0.00004221821,0.0001012949,0.0004888269,0.003115808],"genre_scores_gemma":[0.5500968,0.0005136072,0.4469181,0.0001004727,0.00001968901,0.0002746603,0.0002912121,0.0001716596,0.001613784],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001378919,"threshold_uncertainty_score":0.004612982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06251815708773542,"score_gpt":0.3041285096728862,"score_spread":0.2416103525851508,"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."}}