{"id":"W4290756430","doi":"10.24132/csrn.3201.13","title":"Balanced Feature Fusion for Grouped 3D Pose Estimation","year":2022,"lang":"en","type":"article","venue":"Computer Science Research Notes","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Pose; Computer science; Feature (linguistics); Artificial intelligence; 3D pose estimation; Fusion; Pattern recognition (psychology); Key (lock); Feature extraction; Estimation; Machine learning; Data mining; 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.003431654,0.0001167269,0.0001294075,0.0007119028,0.003016846,0.0008013122,0.001905795,0.00003346744,0.00004592702],"category_scores_gemma":[0.0002444503,0.0001109856,0.00006274979,0.002273852,0.0002514551,0.001386225,0.001545194,0.0003996021,0.00004794278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002311606,"about_ca_system_score_gemma":0.0003612438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001352995,"about_ca_topic_score_gemma":0.000002253421,"domain_scores_codex":[0.9966994,0.0002077219,0.0001697121,0.0007345211,0.001508774,0.0006798509],"domain_scores_gemma":[0.9980721,0.0005726981,0.00006929861,0.0005666668,0.0005412253,0.0001780243],"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":[0.00002659642,0.0001881354,0.00008100818,0.00003521808,0.00000554065,0.00001282621,0.00100631,0.003991836,0.01705417,0.01284038,0.006300096,0.9584579],"study_design_scores_gemma":[0.0003661394,0.0004588798,0.001713725,0.00001906105,0.000001208037,0.00002680383,0.00001852934,0.9753292,0.005677475,0.01218903,0.004040374,0.0001595798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03761243,0.00004572863,0.9567929,0.003573994,0.0009862211,0.0005859717,0.000008483861,0.0001886485,0.0002056256],"genre_scores_gemma":[0.7287684,0.000007387462,0.2702258,0.0003880348,0.0002660306,0.0001952173,0.00002021325,0.000008494405,0.0001204675],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9713374,"threshold_uncertainty_score":0.9982811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09510805036543139,"score_gpt":0.3896675337787052,"score_spread":0.2945594834132738,"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."}}