{"id":"W3174046134","doi":"10.3390/app11135806","title":"Automatic Wheels and Camera Calibration for Monocular and Differential Mobile Robots","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"JetBrains Research","keywords":"Computer vision; Artificial intelligence; Computer science; Robot calibration; Camera auto-calibration; Robot; Calibration; Camera resectioning; Mobile robot; Position (finance); Process (computing); Robot kinematics; Mathematics","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":[0.00005956123,0.00006365474,0.00008576729,0.00002864886,0.0001247383,0.0001267098,0.00003206149,0.00003114235,0.00001421112],"category_scores_gemma":[0.000006053983,0.00005766214,0.00001035214,0.0001041928,0.00006480088,0.00007084222,0.00001228296,0.00002213146,5.434105e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000590281,"about_ca_system_score_gemma":0.00001234208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002719939,"about_ca_topic_score_gemma":0.000006464186,"domain_scores_codex":[0.9995667,0.000006164034,0.00009406079,0.0001424956,0.00008373573,0.0001068325],"domain_scores_gemma":[0.9998444,0.00004102129,0.00001279397,0.00005298944,0.00001177912,0.00003698601],"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":[0.000002263886,0.0000231916,0.0004074363,0.0001918315,0.00002283707,0.000001667249,0.0007509135,0.7652464,0.1758863,0.01755132,0.0002266133,0.03968921],"study_design_scores_gemma":[0.0001387694,0.00001637538,0.0003670868,0.000006535716,0.000009460465,0.000001582696,0.0001271082,0.9754474,0.02273426,0.0009994936,0.0000717842,0.00008015146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7443589,0.0002518712,0.2548079,0.00003937937,0.00008062973,0.0001712057,0.000002471775,0.00005876074,0.0002288516],"genre_scores_gemma":[0.9921442,0.00004323867,0.007691744,0.00003052081,0.0000266044,0.00003474476,0.00000987009,0.000006493132,0.00001254872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2477853,"threshold_uncertainty_score":0.2351394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01076350637045274,"score_gpt":0.2171666313407186,"score_spread":0.2064031249702658,"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."}}