{"id":"W4389500786","doi":"10.48550/arxiv.2312.03806","title":"XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Voxel; Computer science; Generative model; Generative grammar; Variety (cybernetics); Resolution (logic); Scale (ratio); Artificial intelligence; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003984825,0.0004024175,0.0004134352,0.0007982858,0.0004011792,0.0003486502,0.001811435,0.0002894501,0.000007414385],"category_scores_gemma":[0.00001401547,0.0004847903,0.0002637753,0.001222781,0.00007896815,0.0004606552,0.004481583,0.0005811843,0.00002023413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001465835,"about_ca_system_score_gemma":0.0002351662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000220838,"about_ca_topic_score_gemma":0.0001072209,"domain_scores_codex":[0.9974577,0.0001956196,0.0003143066,0.001391596,0.0001609447,0.0004798553],"domain_scores_gemma":[0.9979658,0.00005988646,0.0002353039,0.001262533,0.0003059304,0.0001705148],"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":[0.000004723736,0.00007148179,0.0005581671,0.00004672381,0.00006589796,0.0001059742,0.0008860332,0.6327326,0.00002689217,0.3652032,0.0001719565,0.0001263609],"study_design_scores_gemma":[0.0001835543,0.00002671899,0.00003141565,0.0001161566,0.0000317563,0.000002601866,0.00005261915,0.8817267,0.0001560303,0.117063,0.0001461216,0.0004632695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1056156,0.00005506435,0.8921191,0.0000471779,0.0006495831,0.0002808896,0.00003102241,0.001067341,0.000134214],"genre_scores_gemma":[0.9351689,0.0004569285,0.06339,0.0001479875,0.0001742737,0.000002209803,0.00004147648,0.00004834411,0.0005698411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8295534,"threshold_uncertainty_score":0.9997604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1674262106397581,"score_gpt":0.2507127308792736,"score_spread":0.08328652023951552,"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."}}