{"id":"W2152445702","doi":"10.1109/crv.2006.32","title":"Evolving a Vision-Based Line-Following Robot Controller","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; OpenGL; Mobile robot; Robot; Controller (irrigation); Evolutionary computation; Artificial intelligence; Simple (philosophy); Computation; Line (geometry); Computer vision; Ubiquitous robot; Genetic programming; Robot control; Human–computer interaction; Computer graphics (images); Visualization; Programming language; 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":[0.0001453741,0.00009022533,0.0001038542,0.00006235114,0.000225159,0.0001304789,0.0003851439,0.00003557718,0.00003952995],"category_scores_gemma":[0.0000119929,0.00007616709,0.0001108214,0.0003283657,0.00001528957,0.0003074968,0.00006374456,0.00006271453,0.0001168613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003124278,"about_ca_system_score_gemma":0.00005478156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000152407,"about_ca_topic_score_gemma":0.00001195018,"domain_scores_codex":[0.9991311,0.00001893684,0.0001851358,0.0002719118,0.0001978985,0.0001949971],"domain_scores_gemma":[0.9994244,0.0001315473,0.00003848296,0.0002959328,0.00006103534,0.00004857451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000688046,0.001074139,0.003673196,0.00001233775,0.00004649938,0.00003676233,0.00006058304,0.1543425,0.02582698,0.7312214,0.05192216,0.0317766],"study_design_scores_gemma":[0.0004846315,0.00002383253,0.007229922,0.000008030547,0.000003230668,0.000001512092,0.000002775799,0.9814045,0.0003803723,0.006660435,0.003681676,0.0001190258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001239645,0.0001750534,0.9851858,0.003389148,0.0001000189,0.0001283829,7.274571e-7,0.0002765994,0.0095046],"genre_scores_gemma":[0.7337101,3.602746e-7,0.2642611,0.0003784916,0.00009319289,0.00002908334,0.000002942571,0.000004493257,0.001520339],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8270621,"threshold_uncertainty_score":0.3106004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007712572380717798,"score_gpt":0.2454616681545138,"score_spread":0.237749095773796,"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."}}