{"id":"W4415353261","doi":"10.1109/lra.2025.3623436","title":"Breaking the Static Assumption: A Dynamic-Aware LIO Framework via Spatio-Temporal Normal Analysis","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Consistency (knowledge bases); Point cloud; Point (geometry); Face (sociological concept); Perspective (graphical); Identification (biology); Dynamic data; Dependency (UML)","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":[0.0007069235,0.0009668874,0.001224923,0.001335779,0.0008340687,0.001608065,0.002595128,0.0007499033,0.001285689],"category_scores_gemma":[0.002848357,0.0006312116,0.0006517057,0.001496277,0.001238247,0.003369651,0.003798057,0.001452144,0.0008475161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007087691,"about_ca_system_score_gemma":0.001901491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007865044,"about_ca_topic_score_gemma":0.007803629,"domain_scores_codex":[0.999106,0.0001250311,0.00003077737,0.0002076037,0.0004117919,0.0001188979],"domain_scores_gemma":[0.9990809,0.0001729255,0.0001732676,0.0002054635,0.0002696356,0.00009780296],"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":[0.0002357894,0.0001699925,0.002740533,0.0001356272,0.00007458448,0.0003329333,0.0005490755,0.5312592,0.02190875,0.06812406,0.004987106,0.3694824],"study_design_scores_gemma":[0.000005212323,0.00002168496,0.0001605384,0.000004797752,0.000005596055,0.00005184123,0.00005124214,0.9862275,0.001404489,0.009729593,0.002320096,0.00001740672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005217537,0.00008647033,0.9929326,0.00006605719,0.0000268428,0.00001958013,0.00003212586,0.0005586979,0.001060231],"genre_scores_gemma":[0.4347419,0.0004343639,0.5587515,0.0001343987,0.0001374725,0.000133406,0.0004837377,0.0005627201,0.004620546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007865044,"threshold_uncertainty_score":0.01563853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02401110004656101,"score_gpt":0.3512024607141185,"score_spread":0.3271913606675575,"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."}}