{"id":"W4410907428","doi":"10.21428/d82e957c.55183e34","title":"The Finer Points: A Systematic Comparison of Point-Cloud Extractors for Radar Odometry","year":2025,"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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Odometry; Point cloud; Radar; Remote sensing; Point (geometry); Computer science; Cloud computing; Artificial intelligence; Geodesy; Environmental science; Geology; Mathematics; Telecommunications; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002836076,0.002160389,0.001205564,0.005370517,0.0007768202,0.002044269,0.001506034,0.001064747,0.002338787],"category_scores_gemma":[0.01049077,0.0006278508,0.002155254,0.005230134,0.0006346908,0.003192922,0.00206736,0.001293893,0.003503429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000426202,"about_ca_system_score_gemma":0.001106759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01011872,"about_ca_topic_score_gemma":0.01924878,"domain_scores_codex":[0.9973217,0.0002154319,0.0003463219,0.0006431035,0.001246925,0.0002265701],"domain_scores_gemma":[0.9966583,0.001049756,0.0002562699,0.0007032319,0.001230461,0.0001020301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0012124,0.0002189916,0.04476205,0.002222955,0.001200638,0.0001671422,0.0004353727,0.03137646,0.02772802,0.001530454,0.01732556,0.87182],"study_design_scores_gemma":[0.0003918064,0.001650413,0.2466116,0.001569668,0.00160908,0.002393896,0.003265664,0.3624734,0.1918215,0.008097877,0.1794965,0.0006186132],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3535443,0.02400237,0.5592853,0.0009473115,0.0009063713,0.00102403,0.02164464,0.02685308,0.01179259],"genre_scores_gemma":[0.354198,0.009694917,0.5752798,0.0003316656,0.0001694958,0.0003426008,0.04993286,0.003915011,0.006135653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01011872,"threshold_uncertainty_score":0.02011967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156755425474419,"score_gpt":0.259903428351812,"score_spread":0.2483358740970678,"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."}}