{"id":"W1979582726","doi":"10.1002/vis.311","title":"NRC 3D imaging technology for museum and heritage applications","year":2003,"lang":"en","type":"article","venue":"The Journal of Visualization and Computer Animation","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Cultural heritage; Computer science; Variety (cybernetics); National heritage; High resolution; Computer graphics (images); Data science; Library science; Remote sensing; Artificial intelligence; Archaeology; History; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005341082,0.00005042341,0.00007338452,0.00005422641,0.0001831745,0.00005252727,0.00005100888,0.00002099327,0.00001929023],"category_scores_gemma":[0.00001958427,0.00003256441,0.00001278564,0.0001225662,0.00003729546,0.0001656967,0.000003344533,0.00003965297,0.00000135939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002205911,"about_ca_system_score_gemma":0.00001122297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005019367,"about_ca_topic_score_gemma":0.00002114647,"domain_scores_codex":[0.99956,0.00007324216,0.0001631969,0.00005148945,0.00008582662,0.00006622384],"domain_scores_gemma":[0.9996099,0.00006663815,0.0001199808,0.00003853234,0.0001374972,0.00002746493],"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":[0.0001400222,0.00009585071,0.1768632,0.0002162114,0.00008474629,0.000002514507,0.005431223,0.001742146,0.001229637,0.0555844,0.002158816,0.7564512],"study_design_scores_gemma":[0.001852625,0.0008534804,0.1896538,0.0001250667,0.0001312471,0.0009865923,0.003572003,0.7056537,0.0005747743,0.01386236,0.08228355,0.0004507908],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1570777,0.002042211,0.8399249,0.0003269474,0.0001019404,0.0002021462,0.000005266584,0.00002330773,0.0002954786],"genre_scores_gemma":[0.9949247,0.0002344614,0.004617536,0.0001475799,0.0000493286,7.421931e-7,0.000006399162,0.000001971199,0.00001725879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.837847,"threshold_uncertainty_score":0.1408849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01325946279991191,"score_gpt":0.2505603611944957,"score_spread":0.2373008983945838,"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."}}