{"id":"W2612843093","doi":"10.1145/3072959.3073637","title":"GRASS","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":314,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Google; National Natural Science Foundation of China; Qualcomm; National Science Foundation","keywords":"Computer science; Hierarchy; Encoding (memory); Context (archaeology); Adjacency list; Key (lock); Pipeline (software); Topology (electrical circuits); Interpolation (computer graphics); Code (set theory); Matching (statistics); Artificial intelligence; Net (polyhedron); Theoretical computer science; Algorithm; Geometry; Image (mathematics); Mathematics; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00005964398,0.00009253835,0.00009420928,0.0001186029,0.00044946,0.00007718115,0.0003513254,0.00006674021,0.00003509621],"category_scores_gemma":[0.00001215368,0.00009217866,0.0001304938,0.00007827399,0.00003937701,0.0001005537,0.000001669935,0.0002042871,0.00005373375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009263897,"about_ca_system_score_gemma":0.000003399678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002690296,"about_ca_topic_score_gemma":0.000138479,"domain_scores_codex":[0.9995585,0.000005231731,0.00009228383,0.0001059804,0.0001044294,0.0001335806],"domain_scores_gemma":[0.9990256,0.00002347329,0.00001470775,0.0008609107,0.0000222159,0.00005313419],"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":[0.00001680357,0.0002142104,0.001580418,0.00009922213,0.0008877278,0.00002590907,0.0003523311,0.7059303,0.001408943,0.001314925,0.001677941,0.2864913],"study_design_scores_gemma":[0.001021865,0.00009636406,0.00903825,0.0001180077,0.0004635886,0.00001202108,0.0001053412,0.9410492,0.009772784,0.02909718,0.008142867,0.001082515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07535989,0.00007880993,0.9203569,0.0007803842,0.0004184763,0.000041208,0.00001944059,0.0004214863,0.00252344],"genre_scores_gemma":[0.9984086,0.0003872579,0.0009379353,0.00006075233,0.00002745119,0.000008790371,0.00000186864,0.00002004868,0.0001473546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9230487,"threshold_uncertainty_score":0.3758937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02621619073944116,"score_gpt":0.2509840160724924,"score_spread":0.2247678253330512,"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."}}