{"id":"W4309089854","doi":"10.3390/rs14215542","title":"3D Point Cloud for Cultural Heritage: A Scientometric Survey","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Beijing University of Civil Engineering and Architecture; China Scholarship Council","keywords":"Cultural heritage; Data science; Citation; Computer science; Tag cloud; Cloud computing; Web of science; Field (mathematics); Point (geometry); World Wide Web; Geography; Visualization; Data mining; Political science; 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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007875455,0.0007450106,0.00105755,0.1131903,0.001644907,0.004748378,0.0007147153,0.0006489618,0.004301219],"category_scores_gemma":[0.02730433,0.0002735589,0.001362088,0.1561371,0.001124806,0.004320711,0.002826658,0.0006854842,0.00124519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001683639,"about_ca_system_score_gemma":0.00303396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01047616,"about_ca_topic_score_gemma":0.007693692,"domain_scores_codex":[0.9910924,0.001443797,0.0009399236,0.0007400905,0.005447302,0.0003364351],"domain_scores_gemma":[0.971824,0.01112753,0.003568195,0.002021146,0.01053902,0.0009200406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001133036,0.0001186227,0.2635568,0.006445059,0.0006518969,0.0004869745,0.00634304,0.00335177,0.003096968,0.01893707,0.0298753,0.6670232],"study_design_scores_gemma":[0.00001310506,0.0001269042,0.6192404,0.003018486,0.0004471381,0.001348513,0.01365228,0.009934137,0.003086769,0.01158909,0.3373961,0.0001471017],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5999154,0.09718512,0.07920267,0.005914193,0.0006426635,0.001199859,0.07768936,0.001362405,0.1368883],"genre_scores_gemma":[0.8354797,0.06781299,0.04361627,0.0005008075,0.0006066855,0.001053579,0.0458632,0.0003067752,0.004760015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8868098,"threshold_uncertainty_score":0.04164988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05919526096422081,"score_gpt":0.2758052061187811,"score_spread":0.2166099451545603,"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."}}