{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001651816,0.002033027,0.001999278,0.003481627,0.0005629572,0.001605243,0.002370595,0.001595149,0.0018612],"category_scores_gemma":[0.003488521,0.0007747495,0.001975598,0.002342524,0.0006820347,0.001882852,0.00342884,0.001370609,0.001507645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008021811,"about_ca_system_score_gemma":0.001154696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004668616,"about_ca_topic_score_gemma":0.003553107,"domain_scores_codex":[0.9980568,0.0002003415,0.00006218684,0.0005858712,0.0008779411,0.0002168113],"domain_scores_gemma":[0.9988457,0.0002483923,0.0001119144,0.0002618584,0.0004591522,0.00007291059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005338065,0.000178015,0.002777045,0.0001692512,0.000212673,0.0003148266,0.0001534184,0.04822605,0.07153607,0.003041632,0.005646814,0.8672104],"study_design_scores_gemma":[0.00004338708,0.000213912,0.002575482,0.00002246301,0.00009370905,0.0003877981,0.00005752812,0.9436013,0.04353773,0.004402333,0.00500447,0.00005985722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01851025,0.000474188,0.978146,0.00007844802,0.00009413969,0.00009929507,0.0002008327,0.001719736,0.0006773234],"genre_scores_gemma":[0.3970929,0.000604,0.5964241,0.0001497244,0.0001515442,0.0002216802,0.002062676,0.0003256888,0.002967774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004668616,"threshold_uncertainty_score":0.009282887,"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."}}