{"id":"W3032090376","doi":"10.1016/j.eswa.2020.113559","title":"Advancement of the search process for digital heritage by utilizing artificial intelligence algorithms","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"A Thinking Ape (Canada)","funders":"","keywords":"Big data; Computer science; Process (computing); Cultural heritage; Data science; Artificial intelligence; Algorithm; Data mining; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000728654,0.0005953739,0.0007116436,0.003155603,0.0007202781,0.002420577,0.000930118,0.0009371691,0.004507896],"category_scores_gemma":[0.001941257,0.000246916,0.0008119355,0.002057495,0.0006893661,0.002266344,0.00117326,0.0007774049,0.001359675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005258872,"about_ca_system_score_gemma":0.001646629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00375611,"about_ca_topic_score_gemma":0.003343906,"domain_scores_codex":[0.9995894,0.00008604236,0.00003074889,0.00008032384,0.0001788014,0.00003463863],"domain_scores_gemma":[0.999448,0.0002019421,0.00006587878,0.0001021037,0.0001494229,0.00003266328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001363092,0.0001907811,0.003756796,0.0009272862,0.00009531582,0.0001450942,0.0003467528,0.06665245,0.02453533,0.06597927,0.004723784,0.8325108],"study_design_scores_gemma":[0.00004920455,0.0002143145,0.003724583,0.0002551991,0.0001612703,0.0005472676,0.0006432634,0.8368446,0.02972698,0.08242738,0.04533013,0.00007577742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03524972,0.003435303,0.9426053,0.0006061013,0.0001271039,0.0001146963,0.0001722406,0.0008036037,0.01688594],"genre_scores_gemma":[0.296276,0.004817171,0.6912796,0.0001368129,0.0001395912,0.0000748415,0.0005639735,0.0001172638,0.00659466],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004507896,"threshold_uncertainty_score":0.01508045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04532716793122469,"score_gpt":0.3037497666552349,"score_spread":0.2584225987240102,"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."}}