{"id":"W2113635357","doi":"10.1109/tip.2003.822592","title":"High-Accuracy 3-D Modeling of Cultural Heritage: The Digitizing of Donatello's “Maddalena”","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Centre National de la Recherche Scientifique","keywords":"Cultural heritage; Photogrammetry; Computer science; Computer vision; Iterative closest point; Artificial intelligence; Computer graphics (images); Calibration; 3D modeling; Digital elevation model; Process (computing); Focus (optics); Point cloud; Remote sensing; Archaeology; Geography; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002163481,0.000171636,0.000219623,0.00005304089,0.0004030755,0.0001084854,0.0002387762,0.0000602867,0.0001108049],"category_scores_gemma":[0.00001734363,0.000108954,0.0001162742,0.0003487574,0.0001931927,0.0008509347,7.360082e-7,0.0002615195,0.00001602259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009126276,"about_ca_system_score_gemma":0.00007123577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001628227,"about_ca_topic_score_gemma":0.0003682,"domain_scores_codex":[0.9988096,0.00005244631,0.0003720839,0.0002185893,0.0002939017,0.0002533333],"domain_scores_gemma":[0.9993885,0.00008730324,0.0001397617,0.0001615023,0.0001615592,0.00006134927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001069182,0.00008737111,0.0001794994,0.0002401386,0.00003600756,0.000009432056,0.003718799,0.7506152,0.01077615,0.000007809461,0.000004012246,0.2342187],"study_design_scores_gemma":[0.004729194,0.000997866,0.005673671,0.002934172,0.0003936952,0.0002901473,0.03706151,0.4150568,0.5262887,0.00413757,0.0002123465,0.00222427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8672788,0.0007556206,0.1300162,0.0001967288,0.0002307708,0.0001486258,0.00009110101,0.00007920602,0.00120301],"genre_scores_gemma":[0.9947166,0.000100005,0.005003848,0.00004777943,0.00003165571,0.000002240089,0.0000103507,0.000007463972,0.00007999838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5155126,"threshold_uncertainty_score":0.4443017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0250764935353347,"score_gpt":0.2438818055295114,"score_spread":0.2188053119941767,"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."}}