{"id":"W105450294","doi":"","title":"A Pragmatic Global Vision System for Educational Robotics","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Robotics; Artificial intelligence; Computer science; Machine vision; Robot; Cognitive neuroscience of visual object recognition; Robot vision; Object (grammar); Overhead (engineering); Server; Computer vision; Human–computer interaction; Mobile robot; World Wide Web","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":[0.0009513216,0.0006858256,0.0005678616,0.0009844904,0.0007544157,0.001763318,0.001261398,0.001251763,0.02219443],"category_scores_gemma":[0.001770777,0.000319396,0.0003777537,0.0005857052,0.0007172601,0.001782955,0.002666347,0.001287984,0.01071461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006521005,"about_ca_system_score_gemma":0.001255825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001360585,"about_ca_topic_score_gemma":0.002036487,"domain_scores_codex":[0.9991793,0.000174778,0.00003962538,0.000137326,0.0003966351,0.00007228526],"domain_scores_gemma":[0.9994959,0.00006993613,0.00002348289,0.0001002697,0.0002090204,0.0001014408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002283871,0.0001474896,0.0005277264,0.0003316207,0.00002850743,0.0002423173,0.0004469045,0.003385196,0.04749313,0.1005211,0.04858565,0.7980619],"study_design_scores_gemma":[0.0001655536,0.0005521569,0.001566775,0.0001450012,0.00005914216,0.001286001,0.0001980299,0.08178055,0.03000462,0.0544112,0.8296901,0.0001408573],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002507888,0.0003822506,0.9531219,0.0005253488,0.0002033266,0.000219584,0.0001812599,0.01511291,0.02774552],"genre_scores_gemma":[0.1090177,0.0005826144,0.8499123,0.001006399,0.0001979955,0.0006212065,0.001050737,0.001616998,0.03599405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02219443,"threshold_uncertainty_score":0.07424772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05808737164227477,"score_gpt":0.3414758550813853,"score_spread":0.2833884834391105,"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."}}