{"id":"W4392839494","doi":"10.2316/j.2024.206-1063","title":"DDETR-SLAM: A TRANSFORMER-BASED APPROACH TO POSE OPTIMISATION IN DYNAMIC ENVIRONMENTS, 407-421.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Transformer; Computer science; Artificial intelligence; Computer vision; Engineering; Electrical engineering; Voltage","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":[0.0001937633,0.0001005844,0.000116408,0.0002920731,0.00001350743,0.0001137068,0.0001095457,0.00005723095,0.00001085866],"category_scores_gemma":[0.00001084461,0.00008658251,0.00005035529,0.0001009171,0.000007415898,0.0002236994,0.000006539316,0.0001304681,0.000004183101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001882715,"about_ca_system_score_gemma":0.000021708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002790934,"about_ca_topic_score_gemma":0.000004144828,"domain_scores_codex":[0.9991519,0.00001176274,0.000359312,0.00008843427,0.0002928955,0.00009570983],"domain_scores_gemma":[0.9997962,0.00003496806,0.00004241716,0.00003943993,0.00003023816,0.00005677628],"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":[0.000008610185,0.00003839352,0.0000170835,0.00003035143,0.00003725497,0.00001320797,0.0001939812,0.9563293,0.006492147,0.003548701,0.00002737878,0.03326353],"study_design_scores_gemma":[0.0002716284,0.0000500728,0.001216181,0.0001496994,0.00001628197,0.00004853996,0.00004239234,0.9965293,0.0001926038,0.001186603,0.0002013019,0.00009540155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03260463,0.0001874664,0.9653208,0.0006942429,0.0008037434,0.00009921545,0.00000809592,0.00003201312,0.000249784],"genre_scores_gemma":[0.8571152,0.000155733,0.1425287,0.00004722486,0.00007722868,0.000003382457,0.00002906513,0.00001321157,0.00003027927],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8245106,"threshold_uncertainty_score":0.3530732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00634937286587795,"score_gpt":0.2236352834717118,"score_spread":0.2172859106058339,"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."}}