{"id":"W2138985599","doi":"10.1117/1.2955245","title":"Review of measurement quality metrics for range imaging","year":2008,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; National Research Council Canada","funders":"National Research Council Canada","keywords":"Computer science; Image resolution; Image quality; Range (aeronautics); Quality (philosophy); Artificial intelligence; Measurement uncertainty; Computer vision; Remote sensing; Data mining; Image (mathematics); Statistics; Mathematics; Geography; Engineering","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":[0.01781375,0.002049147,0.003583877,0.01440149,0.001249305,0.004268153,0.003880665,0.002309559,0.005079873],"category_scores_gemma":[0.05951441,0.0008746964,0.001499951,0.01502294,0.00165333,0.005636465,0.001842791,0.001591438,0.002794484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003070908,"about_ca_system_score_gemma":0.004444669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004731635,"about_ca_topic_score_gemma":0.003086414,"domain_scores_codex":[0.9784057,0.00570034,0.003366819,0.001400958,0.01082616,0.000299988],"domain_scores_gemma":[0.9438359,0.03010247,0.004162102,0.001808141,0.01972535,0.000366005],"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":[0.00006506209,0.00004858215,0.001504828,0.01525037,0.0001636558,0.0001484108,0.0001961801,0.002444479,0.00123683,0.0238123,0.03624938,0.9188799],"study_design_scores_gemma":[0.0000303595,0.0002812955,0.006140709,0.0182404,0.0005091982,0.002149299,0.00049643,0.008342233,0.005502838,0.03206128,0.9259325,0.0003134289],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001454801,0.878343,0.1014601,0.003069492,0.001395834,0.0002907489,0.0008345984,0.000411687,0.0127397],"genre_scores_gemma":[0.02701708,0.7956951,0.1665802,0.00140541,0.001645543,0.000827567,0.002282403,0.0003464474,0.004200266],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01781375,"threshold_uncertainty_score":0.09420919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04353180472919627,"score_gpt":0.3256435911739738,"score_spread":0.2821117864447776,"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."}}