{"id":"W4366984291","doi":"10.1021/acsami.3c02564","title":"Deep Generative Modeling of Infrared Images Provides Signature of Cracking in Cross-Linked Polyethylene Pipe","year":2023,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada); University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Autoencoder; Pattern recognition (psychology); Infrared; Materials science; Artificial intelligence; Chemical imaging; Degradation (telecommunications); Biological system; Generative model; Polyethylene; Computer science; Representation (politics); Deep learning; Generative grammar; Computer vision; Optics; Physics; Composite material","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":[0.0002976183,0.0003859343,0.0002186147,0.0003242769,0.0001038203,0.0002767615,0.0003605449,0.0003975402,0.0004525291],"category_scores_gemma":[0.0005742575,0.0002622776,0.0005069168,0.0002019141,0.0004170511,0.0004733916,0.0002957131,0.0005605491,0.00009648164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003923842,"about_ca_system_score_gemma":0.0002637925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003158039,"about_ca_topic_score_gemma":0.003407308,"domain_scores_codex":[0.999934,0.00001124664,0.000001952523,0.00002222506,0.0000162864,0.00001430113],"domain_scores_gemma":[0.9998251,0.00008004573,0.00003132508,0.00002049164,0.00002917773,0.00001382575],"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":[0.0001386144,0.00008414536,0.008115621,0.00004459845,0.00004553037,0.0001903307,0.00008495832,0.9162196,0.05047851,0.002442155,0.0006796001,0.02147622],"study_design_scores_gemma":[0.000001296205,0.00000893113,0.001347365,0.000001236303,0.000002616369,0.00001259605,0.000004978266,0.9959041,0.002220373,0.0004371493,0.00005598051,0.000003397085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7635504,0.0002494757,0.2337956,0.0002586939,0.00001891333,0.00001655825,0.0002208749,0.0005555479,0.001334057],"genre_scores_gemma":[0.987825,0.00008904718,0.01087762,0.00003497762,0.000005477053,0.000008223112,0.0002369266,0.00004496418,0.0008776734],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003158039,"threshold_uncertainty_score":0.00627929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01665683349033916,"score_gpt":0.291380459429443,"score_spread":0.2747236259391039,"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."}}