{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001257804,0.0005912103,0.000344366,0.002867979,0.001152563,0.002361443,0.001211888,0.001212333,0.0415077],"category_scores_gemma":[0.001951816,0.000439159,0.0005918257,0.002601897,0.0005583974,0.0008473045,0.001532935,0.001141295,0.01273959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002963402,"about_ca_system_score_gemma":0.004655455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1121481,"about_ca_topic_score_gemma":0.1449859,"domain_scores_codex":[0.998601,0.0001189214,0.00003730733,0.00007431467,0.001089726,0.00007881748],"domain_scores_gemma":[0.9979532,0.0001565378,0.00005400774,0.0002953708,0.001383889,0.0001570122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001432558,0.00003891982,0.001170401,0.0005140787,0.0000165224,0.0004465428,0.0005195955,0.003860391,0.05246858,0.02640593,0.281217,0.6331987],"study_design_scores_gemma":[0.00001625737,0.00002698577,0.002591267,0.0001552193,0.00001651843,0.0006459696,0.00007474682,0.006691489,0.01552209,0.002718297,0.9714876,0.00005362986],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01667997,0.01290267,0.439163,0.005632485,0.002271702,0.0007831107,0.009001909,0.02114368,0.4924215],"genre_scores_gemma":[0.133026,0.01424745,0.505523,0.001890693,0.0004555878,0.0006352828,0.01241944,0.002594384,0.3292081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1121481,"threshold_uncertainty_score":0.2229907,"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."}}