{"id":"W4414229444","doi":"10.1109/access.2025.3610346","title":"Autonomous Mobile Robot Design and Testing for Data Center Monitoring Mission","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Obstacle; Mobile robot; Robot; Obstacle avoidance; Data center; Mobile robot navigation; Trajectory; Automation; Lidar","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":[0.0003313756,0.0004655209,0.0002603353,0.000402838,0.000303508,0.000353641,0.001311361,0.0005097089,0.002661219],"category_scores_gemma":[0.0006128153,0.0002014731,0.0002089241,0.0001126311,0.0002828494,0.0004074529,0.0003810228,0.0003243647,0.0009016409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003547171,"about_ca_system_score_gemma":0.0007006177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070747,"about_ca_topic_score_gemma":0.001080408,"domain_scores_codex":[0.9996525,0.00004541697,0.00001149591,0.00005450728,0.0001860305,0.00005012924],"domain_scores_gemma":[0.9996302,0.00004758494,0.00005584394,0.00006575796,0.0001478909,0.00005267936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005613485,0.0005801762,0.01011679,0.0007763589,0.00006794147,0.0007766759,0.0005830986,0.1040071,0.598282,0.005099592,0.005417043,0.2737318],"study_design_scores_gemma":[0.0002540986,0.006924818,0.01633716,0.0001051862,0.0001079585,0.001148732,0.0003823493,0.5778373,0.341645,0.001854794,0.05329072,0.0001118386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4170736,0.000376438,0.5599069,0.0003795717,0.0001508875,0.001181446,0.0003311088,0.005707257,0.01489279],"genre_scores_gemma":[0.8633956,0.00008208289,0.1306784,0.00008603722,0.0000114599,0.0005508081,0.0001610385,0.0001179568,0.004916679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002661219,"threshold_uncertainty_score":0.008902669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1119884420402924,"score_gpt":0.346192539498288,"score_spread":0.2342040974579956,"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."}}