{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001115364,0.0004806396,0.0002865755,0.0002839827,0.0004692551,0.0004277468,0.0008146616,0.0004546668,0.001260111],"category_scores_gemma":[0.0002666751,0.0002806457,0.0002645522,0.0001760595,0.0005309494,0.0006816458,0.001214276,0.0003186065,0.0005842792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002514844,"about_ca_system_score_gemma":0.0006001682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00352091,"about_ca_topic_score_gemma":0.005804857,"domain_scores_codex":[0.9998728,0.00001401372,0.000004769197,0.00005437437,0.00002922783,0.00002484346],"domain_scores_gemma":[0.9999053,0.00001039757,0.00001450043,0.00002427236,0.00002887493,0.0000165348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003701614,0.0001862444,0.005886732,0.0003189363,0.00008523388,0.000942538,0.0008811495,0.2317666,0.3298655,0.0125913,0.003974173,0.4131315],"study_design_scores_gemma":[0.00005344967,0.0007586062,0.005217467,0.00004940585,0.00007601744,0.00107995,0.0003760948,0.8971844,0.06790307,0.005307035,0.02189411,0.0001003169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1540874,0.0002802253,0.8344984,0.0001553349,0.00004815277,0.000102873,0.00006027979,0.004088399,0.006678998],"genre_scores_gemma":[0.6520087,0.0001678129,0.3426811,0.0001171357,0.00001565083,0.0001125597,0.00009656429,0.00006134588,0.004739247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00352091,"threshold_uncertainty_score":0.007000804,"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."}}