{"id":"W2414991128","doi":"10.5194/isprs-archives-xli-b3-57-2016","title":"QUALITY ANALYSIS OF 3D SURFACE RECONSTRUCTION USING MULTI-PLATFORM PHOTOGRAMMETRIC SYSTEMS","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photogrammetry; Computer science; 3D reconstruction; Surface reconstruction; Computer vision; Quality (philosophy); Artificial intelligence; Data processing; Point cloud; Surface (topology); Database","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.002656973,0.0008269125,0.0004997753,0.002623401,0.0003233924,0.001656326,0.0005158485,0.0006630029,0.001565585],"category_scores_gemma":[0.005100191,0.0003789674,0.0008624109,0.001726496,0.0007079118,0.000953261,0.001290418,0.0005726222,0.0003866265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005622676,"about_ca_system_score_gemma":0.000528908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001781191,"about_ca_topic_score_gemma":0.001528358,"domain_scores_codex":[0.9969383,0.0004607743,0.00012749,0.0002863909,0.002081904,0.0001051203],"domain_scores_gemma":[0.9965509,0.0008378264,0.00046417,0.0006774038,0.001414271,0.00005539366],"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":[0.000846792,0.0001826897,0.02913213,0.0008049692,0.0003281627,0.0004427427,0.0007273299,0.2415013,0.248069,0.005307391,0.001246187,0.4714113],"study_design_scores_gemma":[0.00003146384,0.0004619774,0.03947134,0.00005900703,0.000117213,0.000722729,0.0003301495,0.7984238,0.1545203,0.002269308,0.003477901,0.0001149178],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2090122,0.0004142836,0.7875729,0.00008703629,0.00004802321,0.0001099938,0.0002630244,0.0008486792,0.001643835],"genre_scores_gemma":[0.7619511,0.0003052155,0.2361684,0.00002341366,0.00001454418,0.00005331952,0.0006442105,0.0001646814,0.0006751857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002656973,"threshold_uncertainty_score":0.01405156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04273285643265353,"score_gpt":0.2733245049863576,"score_spread":0.230591648553704,"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."}}