{"id":"W4393141024","doi":"10.1109/irc59093.2023.00030","title":"Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Footprint; Computer science; Range (aeronautics); Simultaneous localization and mapping; Dynamic range; Artificial intelligence; Computer vision; Mobile robot; Engineering; Robot; Aerospace engineering; Geology","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":[0.002827867,0.0009472598,0.0009899909,0.001873752,0.000580851,0.001334466,0.001025521,0.0007469318,0.000611625],"category_scores_gemma":[0.01198814,0.0003343494,0.0007622986,0.001879505,0.0004094217,0.001650657,0.001377917,0.0007518356,0.0001875167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007791066,"about_ca_system_score_gemma":0.001224644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003233638,"about_ca_topic_score_gemma":0.003655382,"domain_scores_codex":[0.9973635,0.001013632,0.0002342831,0.0004684571,0.0006899158,0.0002300961],"domain_scores_gemma":[0.9948401,0.002916996,0.0005325646,0.0006163973,0.0009431597,0.000150751],"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":[0.0003675387,0.0002869847,0.01466016,0.0003316565,0.0002062647,0.0001572526,0.0001671138,0.6990392,0.0239704,0.00276517,0.002586556,0.2554616],"study_design_scores_gemma":[0.00001825597,0.0001059904,0.003835576,0.00001613559,0.00002433724,0.00006560448,0.0001094689,0.9868352,0.006605273,0.001615469,0.0007564042,0.00001229579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3557313,0.0006395925,0.6371683,0.0003377032,0.00005454699,0.0002994563,0.001213193,0.002439597,0.002116367],"genre_scores_gemma":[0.7319674,0.0001400613,0.2646723,0.00007191075,0.00002555176,0.0003251372,0.002293326,0.0001645476,0.0003397149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003233638,"threshold_uncertainty_score":0.0149554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02691128470185486,"score_gpt":0.2868376563320904,"score_spread":0.2599263716302355,"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."}}