{"id":"W4403816334","doi":"10.1145/3680528.3687634","title":"SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Polygon mesh; Computer science; Theoretical computer science; Vertex (graph theory); Representation (politics); Topology (electrical circuits); Feature learning; Algorithm; Artificial intelligence; Mathematics; Computer graphics (images); Graph","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000513761,0.0002937342,0.0003606037,0.0002504869,0.0001146994,0.001294955,0.0009223178,0.0002330472,0.00001304091],"category_scores_gemma":[0.00004752518,0.0002836685,0.0002872524,0.0003770305,0.00001791458,0.0001316877,0.002991943,0.0004769237,0.00001570053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004228219,"about_ca_system_score_gemma":0.0001153403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001490737,"about_ca_topic_score_gemma":0.000017312,"domain_scores_codex":[0.9979278,0.00008775252,0.0004089658,0.001007375,0.0003002695,0.0002678608],"domain_scores_gemma":[0.998516,0.0001784582,0.0002056344,0.0007352347,0.0002906295,0.00007406012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003140691,0.00002798701,0.000232478,0.0002388432,0.00006720952,0.000007258558,0.0006418962,0.0005873148,0.00008460424,0.9751085,0.01749194,0.005508851],"study_design_scores_gemma":[0.0001047289,0.00009371871,0.0000420758,0.0001587849,0.00002250996,0.00000531051,0.00003363046,0.7378596,0.004197592,0.2437523,0.01337996,0.0003497588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002620304,0.0005290993,0.988067,0.001126501,0.001127118,0.0008905223,0.000006434503,0.002446853,0.003186114],"genre_scores_gemma":[0.6738945,0.0004046968,0.3119462,0.0003347553,0.0003158851,0.0002304291,0.000112254,0.00008250542,0.0126788],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7372723,"threshold_uncertainty_score":0.9999616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03719566225335438,"score_gpt":0.3371040424456619,"score_spread":0.2999083801923075,"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."}}