{"id":"W2085280799","doi":"10.1109/carpi.2012.6473358","title":"Noise characterization of depth sensors for surface inspections","year":2012,"lang":"en","type":"preprint","venue":"","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Payload (computing); Noise (video); Context (archaeology); Computer science; Robot; Computer vision; Artificial intelligence; Acoustics; Real-time computing; Geology; Physics; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001251919,0.0005728621,0.0004188668,0.0006979909,0.0002123331,0.0005650567,0.0006285204,0.0006725893,0.0003291555],"category_scores_gemma":[0.006454652,0.0002866441,0.0003655909,0.0004078271,0.0005680802,0.0007794548,0.0004273692,0.0004557026,0.00009660758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006597891,"about_ca_system_score_gemma":0.000375362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00195298,"about_ca_topic_score_gemma":0.001265776,"domain_scores_codex":[0.9988312,0.0003027026,0.00004296468,0.0001563716,0.0005965006,0.00007016317],"domain_scores_gemma":[0.9977278,0.001446886,0.0002540523,0.0001761978,0.0003464871,0.00004859152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004694665,0.00007696277,0.01521096,0.0002919769,0.00006597511,0.0002012936,0.0002743871,0.753686,0.1467773,0.009859016,0.000402579,0.07268411],"study_design_scores_gemma":[0.000002526134,0.00004882532,0.002833195,0.000008733186,0.000005150472,0.000067607,0.00002245566,0.977411,0.01807862,0.001279797,0.0002292409,0.00001296001],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1914819,0.0004408059,0.806946,0.0001156748,0.00001421327,0.0000201912,0.0001016277,0.0002305106,0.0006490862],"genre_scores_gemma":[0.9652898,0.0002312196,0.03394079,0.0000227006,0.00001102406,0.00002340032,0.000126973,0.00004625255,0.0003078084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00195298,"threshold_uncertainty_score":0.006620884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03316718780086384,"score_gpt":0.2660396546651718,"score_spread":0.232872466864308,"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."}}