{"id":"W4409573368","doi":"10.61091/jcmcc127a-027","title":"Optimisation of digital preservation and 3D reconstruction of frescoes based on image processing algorithms","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fresco; Computer science; Computer vision; Computer graphics (images); Digital image processing; Image processing; Artificial intelligence; Image (mathematics); Algorithm; Art; Art history","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":[0.0005663853,0.0007648107,0.0006325111,0.0006834962,0.0002738571,0.0008096845,0.0006712762,0.0007186716,0.001267531],"category_scores_gemma":[0.001249408,0.0002917245,0.0007629168,0.0004895857,0.0006417861,0.0009548973,0.0007537664,0.000614754,0.0002574187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005309734,"about_ca_system_score_gemma":0.0007394253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002404928,"about_ca_topic_score_gemma":0.001675352,"domain_scores_codex":[0.9995962,0.00005662813,0.00002175328,0.0001067779,0.0001747796,0.00004383108],"domain_scores_gemma":[0.9996847,0.0001062036,0.00005077356,0.00005023092,0.00009102925,0.00001710854],"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":[0.0001609447,0.00006011823,0.001384589,0.0001698554,0.00007441694,0.0001334205,0.0001500462,0.7555252,0.04124332,0.01452724,0.001131066,0.1854397],"study_design_scores_gemma":[0.000005721218,0.000030399,0.0002299955,0.000005661854,0.00001048981,0.0000658854,0.00001558657,0.991855,0.006120885,0.001040509,0.0006123603,0.000007452829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02920385,0.0002474052,0.9678923,0.0000732274,0.00002540888,0.00002396222,0.00001618167,0.0002260539,0.002291552],"genre_scores_gemma":[0.4747041,0.000555536,0.5201049,0.00008788642,0.00002885244,0.00007163257,0.0001129032,0.0001310627,0.004203165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002404928,"threshold_uncertainty_score":0.004781842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150662464388259,"score_gpt":0.2368715913299963,"score_spread":0.2218053448911704,"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."}}