{"id":"W3121121014","doi":"10.1109/pvsc45281.2020.9300634","title":"Rapid and Accurate Thin Film Thickness Extraction via UV-Vis and Machine Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Research Foundation Singapore; Agency for Science, Technology and Research; U.S. Department of Energy","keywords":"Thin film; Materials science; Extraction (chemistry); Computer science; Optoelectronics; Nanotechnology; Chemistry; Chromatography","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":[0.0008895895,0.001098321,0.0006000249,0.0007248935,0.0004249365,0.0008994858,0.0009365442,0.0007987573,0.00188367],"category_scores_gemma":[0.002006614,0.0005824902,0.0003856398,0.000646617,0.0005148339,0.00129983,0.0009046618,0.001362244,0.001205378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004294201,"about_ca_system_score_gemma":0.0003926344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009219412,"about_ca_topic_score_gemma":0.00247943,"domain_scores_codex":[0.9992392,0.00007664924,0.00004001771,0.0001600757,0.0004301005,0.00005388254],"domain_scores_gemma":[0.9989951,0.000421312,0.0001855544,0.0001815038,0.0001969207,0.00001959586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001049126,0.0001219426,0.002245446,0.0002794146,0.00005419614,0.0001287577,0.00005716643,0.008731225,0.9278604,0.001034096,0.001289127,0.05809338],"study_design_scores_gemma":[0.00001313072,0.00009494636,0.001346116,0.00002584117,0.00001699506,0.0001190046,0.00002826915,0.1398889,0.8546149,0.000810508,0.003008678,0.0000326268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4427927,0.003136867,0.5321789,0.0008244052,0.0005304406,0.0001812836,0.001342877,0.006783944,0.01222854],"genre_scores_gemma":[0.768917,0.001341271,0.2250155,0.0002626243,0.00008467052,0.0001256327,0.0005545036,0.0003091754,0.003389553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00188367,"threshold_uncertainty_score":0.006301463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02632671383102436,"score_gpt":0.2705617305460397,"score_spread":0.2442350167150154,"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."}}