{"id":"W4410191620","doi":"10.2139/ssrn.5246895","title":"Lidar-3dgs Lidar Reinforcement for Multimodal Initialization of 3d Gaussian Splats","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lidar; Initialization; Gaussian; Remote sensing; Computer science; Reinforcement; Artificial intelligence; Geology; Computer vision; Engineering; Physics; Structural engineering","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0004771707,0.0003461817,0.0005095899,0.0002157865,0.0001824445,0.00006297776,0.0004712198,0.00025759,0.00002956084],"category_scores_gemma":[0.00008406931,0.0003290761,0.0002812601,0.0001431306,0.0001007448,0.0001042958,0.0003743251,0.002438951,0.000002892013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006228157,"about_ca_system_score_gemma":0.002175682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008648732,"about_ca_topic_score_gemma":0.00002651802,"domain_scores_codex":[0.9971367,0.00003770411,0.0006809317,0.0003519927,0.0002506534,0.001542064],"domain_scores_gemma":[0.998511,0.00009723829,0.0006635176,0.0003994864,0.0002735914,0.00005521431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001640453,0.000119106,0.0007302523,0.00009547089,0.0007068979,8.968411e-7,0.0001077249,0.01846487,0.0002413093,0.8612002,0.0000429822,0.1181262],"study_design_scores_gemma":[0.001153683,0.000329558,0.00004918011,0.0004164898,0.0002149623,0.000005711021,0.000542263,0.006572277,0.005343922,0.9836482,0.001304732,0.000419006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01720609,0.0003073775,0.9772032,0.0004153006,0.0004690512,0.0006660275,0.00007060925,0.00008441394,0.003577973],"genre_scores_gemma":[0.9774785,0.0002751789,0.02060243,0.00002010361,0.0004240511,0.00003369792,0.0001415468,0.00003296022,0.0009915481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9602724,"threshold_uncertainty_score":0.9999161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01171827470625835,"score_gpt":0.2867398672707054,"score_spread":0.275021592564447,"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."}}