{"id":"W6912775799","doi":"10.5281/zenodo.7525412","title":"3D-Aware Semantic-Guided Generative Model for Human Synthesis","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Parks and Wilderness Society","funders":"Horizon 2020 Framework Programme","keywords":"Generative grammar; Set (abstract data type); Field (mathematics); Texture synthesis; Generative model; Code (set theory); Computer graphics; Image synthesis","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008892607,0.0001766124,0.0001990746,0.0001952387,0.006789473,0.0008685421,0.001966998,0.00003547055,0.002584614],"category_scores_gemma":[0.0002974193,0.0001911207,0.0001001625,0.0005083635,0.00008152373,0.0004061693,0.002476249,0.0002004307,0.0003064738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001844441,"about_ca_system_score_gemma":0.000009765741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001368215,"about_ca_topic_score_gemma":5.43474e-7,"domain_scores_codex":[0.9977174,0.0005269371,0.0002794666,0.0006248503,0.0004195085,0.0004318713],"domain_scores_gemma":[0.9985448,0.00006099094,0.0001339456,0.0006320175,0.0004911342,0.0001371836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000515793,0.0003730619,0.000001582791,0.00005764748,0.0001670526,0.00002007597,0.004382609,0.2152428,0.02732666,0.0508852,0.573226,0.1282658],"study_design_scores_gemma":[0.0002791751,0.0001316146,0.00001095638,0.000006533451,0.00001533929,0.00003085114,0.0001387863,0.7469372,0.002656749,0.001138443,0.2484324,0.0002219687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001030735,0.00003206704,0.9863213,0.001316418,0.0001244166,0.0005509605,0.0002367948,0.0005348974,0.009852363],"genre_scores_gemma":[0.9745409,0.0000135986,0.02176671,0.0004552485,0.0002050155,0.000001440016,0.0003967404,0.0007555298,0.001864802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9735102,"threshold_uncertainty_score":0.9983271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06255316187991244,"score_gpt":0.2628244228606647,"score_spread":0.2002712609807523,"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."}}