{"id":"W2105020351","doi":"10.1109/cicn.2010.33","title":"Vision Based Robot Control Using Position Specific Artificial Neural Network","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Artificial intelligence; Artificial neural network; Computer vision; Computer science; Robot; Position (finance); Machine vision; Object (grammar); Set (abstract data type); Control system; Engineering","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":[0.0001985891,0.0001142393,0.0001170512,0.0000635428,0.0002474354,0.0002694207,0.0003136961,0.00004213793,0.0001218162],"category_scores_gemma":[0.00001145502,0.0000987318,0.00006185242,0.0002682137,0.00003657425,0.0006970228,0.00006273991,0.0002149924,0.0000530838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001664491,"about_ca_system_score_gemma":0.00002337978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005477297,"about_ca_topic_score_gemma":0.000005436791,"domain_scores_codex":[0.9989362,0.00004536806,0.0002018588,0.0003169524,0.0002045747,0.000295015],"domain_scores_gemma":[0.9993125,0.00007160745,0.00006650693,0.0003815457,0.0000689403,0.00009890077],"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":[0.00003208327,0.00007669668,0.0002369259,0.000001995138,0.000002573893,0.00002055855,0.00002109057,0.1049389,0.5763676,0.02678621,0.0009224694,0.290593],"study_design_scores_gemma":[0.000292263,0.00003705194,0.0012432,0.00000805621,0.000001821262,0.00001283825,0.000002350127,0.9901469,0.004575658,0.002177774,0.001368063,0.0001340775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01339358,0.00001701114,0.9826415,0.001566024,0.001506072,0.0001157948,4.148427e-7,0.0001924829,0.0005671473],"genre_scores_gemma":[0.6558043,3.121431e-7,0.342533,0.001329035,0.0003075244,9.781805e-7,0.000001245733,0.000006244189,0.00001741471],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.885208,"threshold_uncertainty_score":0.4026166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01857081800303227,"score_gpt":0.2864422198937406,"score_spread":0.2678714018907083,"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."}}