{"id":"W3012176411","doi":"10.1038/s41524-020-00380-w","title":"Quantifying defects in thin films using machine vision","year":2020,"lang":"en","type":"preprint","venue":"npj Computational Materials","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of British Columbia","funders":"Natural Resources Canada; Canada Research Chairs; Canada First Research Excellence Fund; University of British Columbia; Western Canada Research Grid; Compute Canada","keywords":"Convolutional neural network; Computer science; Thin film; Personalization; Software; Artificial intelligence; Image processing; Sensitivity (control systems); Machine vision; Materials science; Computer vision; Image (mathematics); Nanotechnology; Electronic engineering; Engineering","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":[0.0003319552,0.0004727555,0.0003091882,0.0014175,0.0001260383,0.0008028475,0.000481934,0.0006650242,0.001335092],"category_scores_gemma":[0.0009864781,0.0002435155,0.0002395699,0.000528974,0.0003854162,0.0006471617,0.0002986575,0.00034761,0.0002979553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005659053,"about_ca_system_score_gemma":0.0002471381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001956709,"about_ca_topic_score_gemma":0.001549002,"domain_scores_codex":[0.9997608,0.00002520929,0.00001116117,0.00006549528,0.0001188275,0.00001845777],"domain_scores_gemma":[0.9994919,0.0001667601,0.0001096329,0.00005854387,0.0001538345,0.00001928136],"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":[0.0001578244,0.0001066307,0.005096929,0.0003812954,0.00007927526,0.0002241223,0.00006356195,0.1940018,0.5616936,0.0065824,0.002361176,0.2292513],"study_design_scores_gemma":[0.000004082686,0.0000324775,0.002800117,0.00001668278,0.000009228806,0.00007722205,0.00001481337,0.9082441,0.08506297,0.002653325,0.001073471,0.00001162785],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2852613,0.001464034,0.7041437,0.0003039869,0.0001231951,0.00009815778,0.0005389352,0.002373885,0.005692893],"genre_scores_gemma":[0.8203422,0.0004959869,0.17651,0.00007509927,0.000024311,0.00003224353,0.0003681025,0.00008139845,0.002070587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001956709,"threshold_uncertainty_score":0.004466295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.077918200655731,"score_gpt":0.3148924281072619,"score_spread":0.2369742274515309,"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."}}