{"id":"W4404103148","doi":"10.1109/metacom62920.2024.00021","title":"Tuner: A New Approach For 3D Semantic Segmentation Using Federated Architecture","year":2024,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Tuner; Computer science; Architecture; Segmentation; Computer architecture; Artificial intelligence; Information retrieval; Natural language processing; Radio frequency; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.00005338214,0.00009176473,0.00009583509,0.00009630454,0.00004804693,0.0001428029,0.00003124946,0.00004000342,0.00005097967],"category_scores_gemma":[0.000003640517,0.00007683024,0.00006822121,0.0001887204,0.000003257097,0.00004650959,0.000005342145,0.00006774202,0.00001171911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003026556,"about_ca_system_score_gemma":0.00001819818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004252845,"about_ca_topic_score_gemma":0.000008297739,"domain_scores_codex":[0.9995546,0.000005336225,0.0001081606,0.000140618,0.00006432845,0.0001269551],"domain_scores_gemma":[0.9998713,0.00001670577,0.000004395645,0.00005768917,0.00001111391,0.00003885966],"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":[0.000001292731,0.000003259607,0.000003389644,0.0001572564,0.00009372229,8.037262e-7,0.0002127863,0.961934,0.01051132,0.00001710957,0.0009998602,0.02606519],"study_design_scores_gemma":[0.0000837421,0.000003884022,4.465216e-7,0.00002425548,0.00008383377,0.000004683349,0.00007901567,0.9979264,0.001342799,0.0001136342,0.0002356853,0.0001016749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009345762,0.0003926871,0.9878491,0.00004667813,0.00007784382,0.00009851464,0.000003043873,0.0004964625,0.001689919],"genre_scores_gemma":[0.6998818,0.000008669564,0.2976763,0.00003379663,0.0001503413,0.000007851446,0.00005855893,0.00003307857,0.002149655],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.690536,"threshold_uncertainty_score":0.3133046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02723868034998559,"score_gpt":0.2582873277929147,"score_spread":0.2310486474429292,"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."}}