{"id":"W4388569893","doi":"10.20944/preprints202311.0614.v1","title":"3D Object Detection Using Multiple Frame Proposal Features Fusion","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; University of Calgary","funders":"","keywords":"Merge (version control); Computer science; Artificial intelligence; Frame (networking); Fusion; Point cloud; Object detection; Computer vision; Object (grammar); Feature (linguistics); Pattern recognition (psychology); Sensor fusion; Data mining; Information retrieval","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000468914,0.0004826534,0.000417415,0.0002775576,0.0004438074,0.0001145615,0.002024067,0.0005036937,0.00002452245],"category_scores_gemma":[0.0003568437,0.0005057247,0.0002251031,0.0007267373,0.00009682476,0.0003372938,0.007614952,0.001652801,0.0009804593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003225382,"about_ca_system_score_gemma":0.0002202377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003468556,"about_ca_topic_score_gemma":0.000173285,"domain_scores_codex":[0.9961489,0.0002031383,0.0005372344,0.001964786,0.0005667573,0.0005791449],"domain_scores_gemma":[0.9961244,0.0002406249,0.0004882593,0.00276478,0.000207037,0.0001748433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000996025,0.0003922523,0.1243857,0.0004155919,0.0002128963,0.00006987021,0.002190417,0.390697,0.4325801,0.002428489,0.0001123875,0.04641568],"study_design_scores_gemma":[0.0004374916,0.0000349268,0.2265016,0.0003303939,0.00005879771,0.00006958539,0.00003051662,0.5530511,0.1769453,0.03959439,0.001681436,0.001264494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4622444,0.00006632868,0.5319828,0.000464116,0.00176243,0.001397961,0.00001055303,0.001816565,0.0002548236],"genre_scores_gemma":[0.9331337,0.00008670916,0.06519453,0.00009545057,0.0004490678,0.0003824096,0.00002349787,0.00007728588,0.000557368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4708893,"threshold_uncertainty_score":0.9997974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1254677092596467,"score_gpt":0.3589060847646049,"score_spread":0.2334383755049582,"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."}}