{"id":"W4362714855","doi":"10.2316/j.2023.206-0826","title":"MULTI-CONSTRAINT SLAM OPTIMISATION ALGORITHM FOR INDOOR SCENES, 375-382.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Constraint (computer-aided design); Artificial intelligence; Computer vision; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002664327,0.0001045483,0.0001368605,0.0003071848,0.00004747947,0.0001160595,0.0001117287,0.00007349725,0.000006228632],"category_scores_gemma":[0.00007117074,0.0001018067,0.00006785057,0.0001215525,0.00002327506,0.0002191431,0.00001357423,0.00008310141,0.00000509346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007753694,"about_ca_system_score_gemma":0.00003104458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002037312,"about_ca_topic_score_gemma":0.0000015366,"domain_scores_codex":[0.9990911,0.00001424944,0.0004268673,0.00008047337,0.0002692729,0.0001180698],"domain_scores_gemma":[0.9991987,0.00007817295,0.0001586814,0.0000503739,0.0004570122,0.00005703961],"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":[0.000004725607,0.00002106362,0.0001107366,0.0000189234,0.00008084461,0.000007550479,0.0001313281,0.8766714,0.002219633,0.0008412789,0.0004068436,0.1194857],"study_design_scores_gemma":[0.0009076261,0.00004455949,0.002415272,0.00007390464,0.00002310393,0.00004186891,0.00007833086,0.9940993,0.001155034,0.0005079706,0.000546896,0.0001061405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02134725,0.00007455811,0.9762712,0.0005437487,0.001478238,0.0001300954,0.0000254488,0.00009731388,0.00003212902],"genre_scores_gemma":[0.7130545,0.0004492061,0.2857255,0.00006334017,0.0004492836,0.000005547608,0.0001531208,0.00003439917,0.00006503458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6917073,"threshold_uncertainty_score":0.4151555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01909821449922636,"score_gpt":0.2623319913678224,"score_spread":0.2432337768685961,"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."}}