{"id":"W4403747048","doi":"10.48550/arxiv.2409.12256","title":"The Finer Points: A Systematic Comparison of Point-Cloud Extractors for Radar Odometry","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Odometry; Radar; Point (geometry); Cloud computing; Remote sensing; Computer science; Artificial intelligence; Computer vision; Geodesy; Mathematics; Geography; Geometry; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.000300971,0.0002819138,0.000550824,0.0002314526,0.00008779996,0.00007010255,0.0004118534,0.0002564597,0.00001183499],"category_scores_gemma":[0.0001237785,0.0002419814,0.000307917,0.0003844661,0.0000667976,0.00004243515,0.0001817319,0.0004056962,0.00002543828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001826063,"about_ca_system_score_gemma":0.00004548145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002259716,"about_ca_topic_score_gemma":0.00002054762,"domain_scores_codex":[0.9987884,0.00007083725,0.0004552396,0.0003479875,0.00009398134,0.0002435729],"domain_scores_gemma":[0.998485,0.0005241578,0.0001813522,0.0006004871,0.0001340325,0.00007500274],"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":[0.00002282247,0.00003502359,0.0001162537,0.01841782,0.000339945,0.00001170337,0.0001810077,0.9407395,0.00009340067,0.03784002,0.002179011,0.00002351587],"study_design_scores_gemma":[0.0002473095,0.00003492825,0.00001911878,0.002071965,0.0004200285,0.000001057011,0.000498772,0.9879193,0.0004735206,0.007701327,0.0003323189,0.0002803273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1900176,0.002322438,0.799117,0.0001180783,0.004276599,0.001884506,0.000139021,0.0004464247,0.00167834],"genre_scores_gemma":[0.998648,0.0001203693,0.0003319034,0.000007040384,0.0001339632,0.000002777787,0.00004034421,0.00006215011,0.0006533792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8086305,"threshold_uncertainty_score":0.9867716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05285307469613496,"score_gpt":0.199553530524871,"score_spread":0.146700455828736,"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."}}