{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002535914,0.00008704181,0.000178988,0.0002338486,0.00002743914,0.00001907478,0.00007329667,0.00004632502,0.0001346175],"category_scores_gemma":[0.00003111961,0.00008627566,0.00007556991,0.001195007,0.000009701938,0.00006892683,0.00002105193,0.00004243046,0.00001914501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006125472,"about_ca_system_score_gemma":0.00000860278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005936739,"about_ca_topic_score_gemma":0.0001515499,"domain_scores_codex":[0.9992252,0.0000281884,0.0002282914,0.0001312648,0.0002572877,0.0001297463],"domain_scores_gemma":[0.9995794,0.00004972079,0.00003009689,0.0002649654,0.0000540877,0.00002171374],"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":[0.000002101984,0.000007169733,0.0001611199,0.00002416319,0.0002639025,0.00000121168,0.0001813399,0.9946309,0.001884177,0.000478429,0.0001588347,0.002206692],"study_design_scores_gemma":[0.0002170051,0.000008405133,0.003343137,0.000007504163,0.0004081778,1.237024e-7,0.00006525405,0.9946803,0.0009129547,0.0001083102,0.0001534624,0.00009540215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2237491,0.00007743884,0.7726078,0.00004536193,0.0001531806,0.0002078556,0.0001329652,0.0002693321,0.00275694],"genre_scores_gemma":[0.9947644,0.0001654942,0.002530354,0.00001390218,0.0000075599,0.000007831254,0.002466519,0.00001661946,0.00002729982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7710153,"threshold_uncertainty_score":0.351822,"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."}}