{"id":"W4386597326","doi":"10.1109/mmar58394.2023.10242529","title":"Disparity Error in Advanced Vision Sensors","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial intelligence; Benchmark (surveying); Computer vision; Computer science; Feature (linguistics); Matching (statistics); Stereopsis; Feature matching; Feature extraction; Mathematics; Geography; Statistics","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":[0.001060345,0.0006911801,0.0006517007,0.00180282,0.0004030813,0.001475397,0.001143353,0.0007992164,0.0022839],"category_scores_gemma":[0.007375716,0.0002859742,0.000358429,0.00261701,0.0004200788,0.001612749,0.001482792,0.0006457219,0.0006852425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090608,"about_ca_system_score_gemma":0.0006809533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005810146,"about_ca_topic_score_gemma":0.003850991,"domain_scores_codex":[0.9969008,0.0002015449,0.0001177712,0.0006142257,0.002007675,0.000157997],"domain_scores_gemma":[0.998355,0.0003204536,0.0002267081,0.0002747614,0.0007760242,0.00004711066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001184663,0.0002274267,0.03793769,0.001009433,0.0003055937,0.0002595272,0.0002486625,0.1971124,0.09076622,0.01746913,0.009976703,0.6435025],"study_design_scores_gemma":[0.00009020329,0.000896365,0.09429754,0.0004312018,0.0001523539,0.001799519,0.0003362934,0.6734732,0.1557825,0.02085099,0.0516876,0.0002022686],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6075366,0.01128486,0.3434355,0.0006158465,0.0009651498,0.0001912241,0.005233582,0.003209344,0.02752791],"genre_scores_gemma":[0.9219505,0.001252449,0.07006907,0.0001235387,0.00009567667,0.00004473874,0.003644491,0.0001843536,0.002635247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005810146,"threshold_uncertainty_score":0.01155263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01932571806651629,"score_gpt":0.335537781454041,"score_spread":0.3162120633875247,"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."}}