{"id":"W4377832633","doi":"10.18280/ts.400222","title":"Non Invasive Decay Analysis of Monument Using Deep Learning Techniques","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Geology; Environmental science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000371329,0.0006222691,0.0004735446,0.001087881,0.0002111557,0.0006790978,0.0006118859,0.000595596,0.0006616139],"category_scores_gemma":[0.0006424828,0.0001979564,0.0004557412,0.0006466422,0.0002766562,0.0006529811,0.0005415084,0.0006022304,0.0003031809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003986347,"about_ca_system_score_gemma":0.0003891096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00359152,"about_ca_topic_score_gemma":0.005980262,"domain_scores_codex":[0.9997751,0.00002166938,0.00001165509,0.00004198935,0.00009862132,0.00005103391],"domain_scores_gemma":[0.9997557,0.00004234131,0.0000485148,0.0000298548,0.0001081913,0.00001544338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006078511,0.0003053938,0.02107553,0.000344761,0.0001985709,0.000718452,0.0002428307,0.1574194,0.245977,0.002165779,0.003836994,0.5671075],"study_design_scores_gemma":[0.000006952066,0.00009575419,0.01298678,0.00002714513,0.00004133209,0.0002108491,0.00009312847,0.949259,0.03472886,0.001013414,0.001513455,0.00002332227],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4519424,0.001055371,0.541266,0.0002417315,0.00006821387,0.00004916846,0.0004309449,0.001762496,0.003183658],"genre_scores_gemma":[0.9310787,0.0004283092,0.06377516,0.00006484856,0.00002631645,0.00002801076,0.000502194,0.0000585669,0.004037852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00359152,"threshold_uncertainty_score":0.007141232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233989202539828,"score_gpt":0.2778610352293429,"score_spread":0.2555211432039446,"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."}}