{"id":"W2979545157","doi":"10.1109/mmar.2019.8864723","title":"Achievable Stereo Vision Depth Accuracy with Changing Camera Baseline","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial intelligence; Pixel; Computer vision; Stereo camera; Baseline (sea); Computer science; Stereopsis; Computer stereo vision; Gaussian; Depth map; Image (mathematics); Geology; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001065123,0.000124248,0.0001234133,0.0001130924,0.00003763692,0.00005182246,0.00006729399,0.00004413188,0.0003392932],"category_scores_gemma":[0.000008953642,0.00009578672,0.00002294422,0.0002323215,0.000006060497,0.0001751486,0.00001540318,0.0000807422,0.0002315282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002938167,"about_ca_system_score_gemma":0.000008539518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002059566,"about_ca_topic_score_gemma":0.00002953867,"domain_scores_codex":[0.9993383,0.00001340714,0.0001288804,0.0001410103,0.0001400452,0.0002383635],"domain_scores_gemma":[0.9996163,0.0000543101,0.00001825898,0.0002179336,0.00003730747,0.0000558577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001433669,0.00001659783,0.001851136,0.00006426831,0.0000182406,0.00000372991,0.0001019782,0.9855421,0.004469148,0.0005779251,0.0006014587,0.006739135],"study_design_scores_gemma":[0.0004029239,0.00007525242,0.0003979312,0.00005200264,0.000008904845,0.000003920556,0.0001091225,0.9888778,0.005587952,0.000007466049,0.004295606,0.0001810744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3741013,0.00007658551,0.6075349,0.0001132576,0.0002180627,0.0002566593,0.000002444877,0.0003764773,0.01732034],"genre_scores_gemma":[0.9930568,0.00002641298,0.005009843,0.0001832003,0.00005654764,0.000002749325,0.00003932527,0.00004178757,0.001583375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6189555,"threshold_uncertainty_score":0.3906069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006189426347727004,"score_gpt":0.2106610017413051,"score_spread":0.2044715753935781,"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."}}