{"id":"W2467344234","doi":"10.5194/isprs-annals-iii-5-3-2016","title":"STRUCTURAL 3D MONITORING USING A NEW SINUSOIDAL FITTING ADJUSTMENT","year":2016,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Photogrammetry; Residual; Computer science; Frame rate; Range (aeronautics); Mean squared error; Frame (networking); Real-time computing; Simulation; Artificial intelligence; Algorithm; Engineering; Statistics; Mathematics; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004479794,0.0007003863,0.0004318972,0.0009497202,0.0002551818,0.0006215021,0.0007319364,0.0006137333,0.002463373],"category_scores_gemma":[0.001726803,0.000497585,0.0006704825,0.001176305,0.0002525838,0.0008812606,0.0007771664,0.0006713374,0.001282199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003049274,"about_ca_system_score_gemma":0.0004003268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061771,"about_ca_topic_score_gemma":0.001856378,"domain_scores_codex":[0.9992022,0.00008721846,0.00004634117,0.0002763135,0.0003573938,0.00003063616],"domain_scores_gemma":[0.9993062,0.000109512,0.00009438717,0.000164974,0.0002973571,0.00002756959],"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":[0.0001856904,0.0001048997,0.003109548,0.0002625006,0.00009749459,0.00007617233,0.0002926117,0.02645495,0.2523157,0.001637065,0.002115716,0.7133477],"study_design_scores_gemma":[0.00004281263,0.0003655422,0.01127928,0.00002476917,0.00009236652,0.0005117548,0.00008530523,0.8463647,0.1168544,0.0009830644,0.023291,0.00010483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01711795,0.00008116571,0.9805976,0.00004989599,0.00004736584,0.00003707676,0.00006936138,0.001306591,0.0006930326],"genre_scores_gemma":[0.190098,0.0001533394,0.8055566,0.00006218508,0.00004121409,0.00008640497,0.0003994945,0.0003478811,0.003254859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002463373,"threshold_uncertainty_score":0.008240819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08612238785916443,"score_gpt":0.2959159955388745,"score_spread":0.2097936076797101,"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."}}