{"id":"W7131079571","doi":"10.1109/iccvw69036.2025.00329","title":"Latent Representation of Microstructures Using Variational Autoencoders with Spatial Statistics-Space Loss","year":2025,"lang":"","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Representation (politics); Microstructure; Latent variable; Pattern recognition (psychology); Binary number; Term (time); Compression (physics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002160951,0.0003085751,0.000435197,0.0002132007,0.0003158968,0.0003010149,0.0004510709,0.0001149513,0.0002107055],"category_scores_gemma":[0.0001043826,0.0002645121,0.00009132901,0.0008734247,0.0003453072,0.0004666336,0.0002659404,0.0001550063,0.000002951678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128528,"about_ca_system_score_gemma":0.0008689267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003903934,"about_ca_topic_score_gemma":0.0003371582,"domain_scores_codex":[0.9975114,0.0002879794,0.0006100098,0.0007123488,0.0005039565,0.0003742603],"domain_scores_gemma":[0.9979893,0.0002235571,0.0004415558,0.0005212165,0.0007387932,0.00008560134],"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":[0.0001882004,0.0001081406,0.006009319,0.00006757493,0.0003954592,0.00001571378,0.0007426321,0.9200044,0.006111071,0.05173367,0.001007599,0.01361619],"study_design_scores_gemma":[0.0008112931,0.0001156722,0.02473384,0.0001279927,0.0001469069,0.000007331037,0.00005807139,0.9417042,0.02851944,0.003433905,0.00008747982,0.0002538312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002651644,0.0001463699,0.9939394,0.001103563,0.001156336,0.0003757511,0.00008012214,0.00002127239,0.0005255222],"genre_scores_gemma":[0.5581022,0.00003600983,0.4412423,0.0001125203,0.00008377384,0.000002415008,0.00001027657,0.000008005355,0.0004025565],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5554506,"threshold_uncertainty_score":0.9999807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01423573259816015,"score_gpt":0.2632522416575085,"score_spread":0.2490165090593484,"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."}}