{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002247331,0.0004471688,0.0004690127,0.000223534,0.0002471315,0.0004952668,0.0006792254,0.0006123944,0.0008560646],"category_scores_gemma":[0.0003584609,0.0002146928,0.0003301842,0.0002787295,0.0003273333,0.0004981511,0.0003131136,0.000587164,0.0002225358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003751815,"about_ca_system_score_gemma":0.0004548266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003904666,"about_ca_topic_score_gemma":0.003386129,"domain_scores_codex":[0.9997744,0.00002917742,0.00001417089,0.00005941899,0.00009844976,0.0000243581],"domain_scores_gemma":[0.9998744,0.0000277646,0.00002401891,0.00001150988,0.00005477082,0.00000747853],"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":[0.0001734603,0.0001447026,0.0007010291,0.000278319,0.0001029149,0.0002849101,0.000104141,0.4799052,0.1044291,0.0124044,0.003165883,0.398306],"study_design_scores_gemma":[0.00002596294,0.0001193315,0.000439142,0.000009062245,0.00002006042,0.00006183437,0.000004654988,0.9895695,0.006434242,0.001351311,0.001948445,0.00001638774],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01765886,0.0006490083,0.9761428,0.00009231599,0.0001349078,0.00004104557,0.00001760368,0.000974824,0.004288679],"genre_scores_gemma":[0.776871,0.0007108113,0.2117496,0.000202182,0.0001233954,0.0001976201,0.0001149795,0.00004751734,0.009983038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003904666,"threshold_uncertainty_score":0.007763863,"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."}}