{"id":"W4415077111","doi":"10.1111/cgf.70257","title":"TopoGen: Topology‐Aware 3D Generation with Persistence Points","year":2025,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China; Ministry of Education - Singapore","keywords":"Controllability; Embedding; Persistence (discontinuity); Topology (electrical circuits); Generative grammar; Point (geometry)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004720692,0.0005115682,0.000561539,0.0006244417,0.0003646851,0.00123527,0.001297417,0.0007948232,0.004697643],"category_scores_gemma":[0.001422705,0.0004111416,0.0007082718,0.0004046666,0.000917296,0.0009169399,0.00230227,0.0008481402,0.001105212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004314647,"about_ca_system_score_gemma":0.0003605404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006549556,"about_ca_topic_score_gemma":0.0007358785,"domain_scores_codex":[0.9996024,0.00004982655,0.00001302123,0.00008634343,0.000216688,0.00003190633],"domain_scores_gemma":[0.999364,0.0002442349,0.0000513383,0.0002259906,0.00005908726,0.0000554012],"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":[0.0002894351,0.000161252,0.001828596,0.0002444577,0.0000857108,0.0005627614,0.0003669506,0.5662897,0.1111742,0.06399537,0.005775462,0.2492262],"study_design_scores_gemma":[0.00001888496,0.00003871784,0.00009498261,0.000008357812,0.000007779275,0.0001140632,0.00001897644,0.9693518,0.01784366,0.009203502,0.003284072,0.00001510535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02406851,0.0001120667,0.9688088,0.00007063345,0.00005081044,0.00005034522,0.00009898537,0.003824449,0.002915345],"genre_scores_gemma":[0.6421013,0.0001688697,0.3513098,0.0001248894,0.00003781492,0.0001383326,0.000467512,0.001394782,0.004256678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004697643,"threshold_uncertainty_score":0.01571518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0140856395696007,"score_gpt":0.2070224489624111,"score_spread":0.1929368093928104,"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."}}