{"id":"W2953625594","doi":"","title":"Accurate 3D reconstruction of museographic objects using multiple image, VRIC 2003, Laval.","year":2003,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000892173,0.0008607522,0.001008306,0.002030634,0.0005222728,0.001818662,0.0009157725,0.001051003,0.00477514],"category_scores_gemma":[0.001761872,0.0009160697,0.0005836853,0.001378665,0.0005719346,0.001236259,0.001492764,0.0009322264,0.00240427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005701701,"about_ca_system_score_gemma":0.0009461292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01244455,"about_ca_topic_score_gemma":0.02507518,"domain_scores_codex":[0.9994451,0.00007480822,0.00001721255,0.0001211359,0.0002981811,0.00004347513],"domain_scores_gemma":[0.999514,0.00006934525,0.00002355558,0.0001450258,0.000197739,0.00005029496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005834763,0.00009257424,0.00240119,0.0003584714,0.0002044684,0.0005379548,0.0004399906,0.05930745,0.1995374,0.003067419,0.04446696,0.6890027],"study_design_scores_gemma":[0.00007642394,0.0001343481,0.02102099,0.0001138525,0.0001236859,0.002072522,0.0003970407,0.7779896,0.1168788,0.003611878,0.07745088,0.0001301005],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04831852,0.002756313,0.9262562,0.0007438518,0.0004519682,0.0001087007,0.002473673,0.007277683,0.01161303],"genre_scores_gemma":[0.1861982,0.001888543,0.788197,0.00008907096,0.0001210703,0.00006880908,0.004136942,0.00152425,0.01777598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01244455,"threshold_uncertainty_score":0.02474421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248610054017927,"score_gpt":0.2192828852003269,"score_spread":0.1967967846601476,"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."}}