{"id":"W4367178982","doi":"10.1007/s10921-023-00947-9","title":"Using the Unsupervised Mixture of Gaussian Models for Multispectral Non-destructive Evaluation of the Replica of Botticelli’s “The Birth of Venus”","year":2023,"lang":"en","type":"article","venue":"Journal of Nondestructive Evaluation","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs; Ministère des relations internationales et de la Francophonie","keywords":"Multispectral image; Artificial intelligence; Computer science; Mixture model; Nondestructive testing; Feature (linguistics); Feature extraction; Segmentation; Replica; Pattern recognition (psychology); Computer vision; Archaeology; Physics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003647524,0.0001448121,0.000332736,0.0002130572,0.00007511162,0.000006787083,0.0003415881,0.0001058508,0.0000126079],"category_scores_gemma":[0.0002961164,0.00008617478,0.0003039852,0.0007498151,0.0002071326,0.0001827119,0.0000228654,0.0002309135,2.915218e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000113193,"about_ca_system_score_gemma":0.0002364341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002809514,"about_ca_topic_score_gemma":0.000007148304,"domain_scores_codex":[0.9976175,0.0003607858,0.0007624745,0.0001099352,0.001011553,0.0001377452],"domain_scores_gemma":[0.9966273,0.0003505936,0.001035363,0.0003660341,0.001597024,0.00002368822],"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":[0.0002146717,0.00003576492,0.0004805283,0.0001822886,0.0003168476,1.440716e-7,0.005501748,0.4568064,0.52943,0.0007111998,0.00003273818,0.006287601],"study_design_scores_gemma":[0.000926581,0.0001678527,0.04215649,0.0002689692,0.0007940646,0.00001428501,0.001479181,0.6645975,0.2165188,0.07299701,0.00000154254,0.00007781185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738492,0.0002702878,0.02407793,0.00004286986,0.0003253119,0.001183686,0.000158497,0.00001044634,0.00008171352],"genre_scores_gemma":[0.9959673,0.0000292762,0.003858631,0.000002878359,0.00008453802,0.00002812767,0.000003194383,0.00002491784,0.000001127757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3129113,"threshold_uncertainty_score":0.3514105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07534716613477183,"score_gpt":0.3305301936376395,"score_spread":0.2551830275028676,"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."}}