{"id":"W2137259591","doi":"10.1109/smcia.2008.5045959","title":"Range estimation using TDL neural networks and application to image-based visual servoing","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial intelligence; Visual servoing; Robustness (evolution); Computer vision; Computer science; Monocular; Artificial neural network; Homography; Servo; Visualization; Robot; 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":[0.00008555956,0.00008349804,0.00007978614,0.00007348756,0.0002159378,0.00008311135,0.0001364556,0.00001880669,0.000003084516],"category_scores_gemma":[0.0000161753,0.00007875867,0.00001716199,0.0002951642,0.00002345353,0.0007116671,0.00009213312,0.00005841502,0.000007468078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002412996,"about_ca_system_score_gemma":0.00001342046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002524202,"about_ca_topic_score_gemma":0.000001724345,"domain_scores_codex":[0.9993203,0.00002116598,0.0001277651,0.0002525062,0.0001186481,0.0001596054],"domain_scores_gemma":[0.9996092,0.00003961176,0.00004086253,0.00017224,0.00005117623,0.00008693707],"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":[0.00001022171,0.0000301878,0.001684633,0.000008960991,0.000001720293,0.000007413615,0.000162865,0.6193705,0.01002678,0.0004766301,0.00009912849,0.368121],"study_design_scores_gemma":[0.0002057951,0.00001893758,0.002330861,0.000007741619,0.000001250928,0.00002159776,0.000005691409,0.9961866,0.001021862,0.00005249932,0.00004563423,0.0001014994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04824197,0.0000249742,0.9507928,0.0004992956,0.00005429443,0.0001488171,8.911664e-8,0.000155518,0.00008219136],"genre_scores_gemma":[0.5431705,9.275304e-7,0.455839,0.0009492392,0.00002012319,0.000003892183,9.098659e-7,0.000004249905,0.00001117046],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4949538,"threshold_uncertainty_score":0.3211685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650567054958064,"score_gpt":0.3065081474177062,"score_spread":0.2900024768681255,"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."}}