{"id":"W3045601337","doi":"10.1039/d0cp02950e","title":"Deciphering tip-enhanced Raman imaging of carbon nanotubes with deep learning neural networks","year":2020,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Carbon nanotube; Raman spectroscopy; Nanoscopic scale; Materials science; Nanotechnology; Visualization; Deep learning; Artificial neural network; Computer science; Artificial intelligence; Optics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004379014,0.0004483803,0.0002835812,0.0004852226,0.0001679622,0.0004858349,0.0005140525,0.0006691234,0.0005099677],"category_scores_gemma":[0.0009603716,0.0002383144,0.0002922793,0.0003360911,0.0004740809,0.0008204897,0.0005678036,0.000875393,0.0002438972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004209208,"about_ca_system_score_gemma":0.0003424695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009304836,"about_ca_topic_score_gemma":0.002033528,"domain_scores_codex":[0.9998579,0.00002901325,0.000005744583,0.00002894805,0.00006022244,0.0000182082],"domain_scores_gemma":[0.9997572,0.0001149614,0.00004008881,0.00003587566,0.00004002918,0.00001183291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001814204,0.0001435426,0.003316481,0.0002564043,0.00007453881,0.0002335632,0.0001683795,0.379975,0.4216749,0.01575589,0.00113609,0.1770839],"study_design_scores_gemma":[0.000001844021,0.0000139359,0.0004593617,0.000004815596,0.000002806248,0.000023479,0.00001185785,0.9676334,0.02849668,0.003011973,0.0003333611,0.000006451432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.279777,0.0007968578,0.7143339,0.0005273316,0.00003479682,0.00004275757,0.0002038918,0.001197943,0.003085622],"genre_scores_gemma":[0.7078593,0.0006447625,0.2890394,0.0001089583,0.00002343198,0.00004677303,0.0002315714,0.00008904618,0.001956678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009304836,"threshold_uncertainty_score":0.003054082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006714509226715158,"score_gpt":0.2601736588437479,"score_spread":0.2534591496170328,"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."}}