{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005668954,0.0008937739,0.0007248386,0.0006968968,0.0005445833,0.001603863,0.002646668,0.001468806,0.00768759],"category_scores_gemma":[0.002202804,0.001035122,0.001577575,0.0009371977,0.0008356335,0.0009416892,0.001901094,0.001824317,0.002087238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000967988,"about_ca_system_score_gemma":0.0009888357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01105229,"about_ca_topic_score_gemma":0.02055859,"domain_scores_codex":[0.9996749,0.00007984784,0.00001159209,0.00006330391,0.0001431012,0.00002727829],"domain_scores_gemma":[0.9993439,0.0003288684,0.00004209656,0.0001312823,0.00009841168,0.00005550178],"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":[0.00005425766,0.00003654224,0.0008401383,0.00009791176,0.00006355213,0.0001717646,0.0001349258,0.9242594,0.004073864,0.02269847,0.01067002,0.03689909],"study_design_scores_gemma":[0.000008102176,0.000005142352,0.00004112671,0.000005404393,0.000003053997,0.00004028754,0.000007070288,0.9918522,0.0008003631,0.004920393,0.002310284,0.000006550538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003950037,0.0001126611,0.9894319,0.0001666002,0.00003937286,0.00006318096,0.000612807,0.003764673,0.001858786],"genre_scores_gemma":[0.2553202,0.0004676103,0.7290928,0.000417469,0.00007707256,0.0005181985,0.003482407,0.003577469,0.00704685],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01105229,"threshold_uncertainty_score":0.02571756,"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."}}