{"id":"W3203003214","doi":"10.3390/robotics10040110","title":"A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments","year":2021,"lang":"en","type":"article","venue":"Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Context (archaeology); Robot; Computer science; Novelty; Architecture; Human–computer interaction; Variety (cybernetics); Mobile robot; Artificial intelligence","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":[0.00007957708,0.00019632,0.0002386145,0.0001071113,0.00004601859,0.00005131095,0.0001021691,0.0001136303,0.00003069925],"category_scores_gemma":[0.0001072821,0.0002189797,0.00003881058,0.0004361806,0.0000206183,0.00005050753,0.00004633735,0.0002133009,0.00003206087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001526063,"about_ca_system_score_gemma":0.00004304858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007040423,"about_ca_topic_score_gemma":0.00004300643,"domain_scores_codex":[0.9988386,0.00004513997,0.0002919567,0.0002863785,0.0001969411,0.0003409789],"domain_scores_gemma":[0.9994576,0.00003013687,0.00002574,0.0003527113,0.00003357353,0.0001002265],"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":[0.000004777539,0.00004058783,0.0002917099,0.0000526293,0.00001707219,0.00005575699,0.0002423152,0.9183913,0.07903494,0.0002285503,0.00007236711,0.001567949],"study_design_scores_gemma":[0.0007417655,0.00004281029,0.003965976,0.000183009,0.0000426005,0.00003228098,0.00005545236,0.9142951,0.07533978,0.0001948274,0.00454025,0.0005661602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1807517,0.0004908253,0.8164273,0.0008101554,0.0005928258,0.0003900087,0.000007539137,0.00010332,0.0004263945],"genre_scores_gemma":[0.8991306,0.00004784151,0.09912256,0.0002348875,0.0001506011,0.000005220909,0.00005346562,0.0000878614,0.001166975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7183789,"threshold_uncertainty_score":0.8929731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0237021010915076,"score_gpt":0.2161657988445298,"score_spread":0.1924636977530222,"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."}}