{"id":"W4214557247","doi":"10.3390/s22051784","title":"Calibration of Stereo Pairs Using Speckle Metrology","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metrology; Speckle pattern; Calibration; Computer vision; Artificial intelligence; Translation (biology); Computer science; Rotation (mathematics); Speckle noise; Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002250917,0.00005182179,0.00009737128,0.00009384428,0.00006045087,0.00001557846,0.0002971369,0.00001734706,0.0001280582],"category_scores_gemma":[0.0000220503,0.00004965157,0.00003666832,0.0002070544,0.00002812545,0.0001172423,0.000178664,0.00008521562,0.000001510428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003820569,"about_ca_system_score_gemma":0.00002047168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003430854,"about_ca_topic_score_gemma":0.000002303159,"domain_scores_codex":[0.9992845,0.0001119815,0.0001484631,0.0001409261,0.0001970653,0.0001170867],"domain_scores_gemma":[0.9996521,0.00002612116,0.00006120978,0.0002056974,0.0000313482,0.00002347029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008801094,0.0004503451,0.01509925,0.00004758611,0.00009057653,0.00003446495,0.002654825,0.005797505,0.4805488,0.4801889,0.00201172,0.01298803],"study_design_scores_gemma":[0.0002445837,0.001051209,0.000913456,0.00001021662,0.00001305617,0.00001933896,0.0003203575,0.7247146,0.2610527,0.009992967,0.001427982,0.0002396063],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9029363,0.00002185982,0.0911794,0.0004499373,0.0002448431,0.0001069227,0.000002164369,0.0001361083,0.00492244],"genre_scores_gemma":[0.9812876,8.275948e-7,0.01852924,0.00009228573,0.00001520163,0.000003068988,6.350088e-7,0.000003355458,0.00006780145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7189171,"threshold_uncertainty_score":0.2024732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05940312674680388,"score_gpt":0.2753034664644331,"score_spread":0.2159003397176292,"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."}}