{"id":"W2902857675","doi":"10.1145/3272127.3275042","title":"OptCuts","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Army Research Office; Natural Sciences and Engineering Research Council of Canada; Skolkovo Institute of Science and Technology; National Science Foundation","keywords":"Distortion (music); Embedding; Upper and lower bounds; Computer science; Scalability; Algorithm; Bijection; Benchmark (surveying); Distortion function; Range (aeronautics); Scratch; Discontinuity (linguistics); Surface (topology); Topology (electrical circuits); Mathematics; Geometry; Artificial intelligence; Discrete mathematics; Mathematical analysis","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":[0.00003482922,0.0001089351,0.000105671,0.0001749311,0.0001008638,0.00001076021,0.0002022208,0.00006711116,0.0001391789],"category_scores_gemma":[0.000009307698,0.0001060828,0.00009049221,0.0006385759,0.00009486581,0.00009625473,0.00000167715,0.000193321,0.0001015504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002115566,"about_ca_system_score_gemma":0.000002865757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000448849,"about_ca_topic_score_gemma":0.00002658018,"domain_scores_codex":[0.9994705,0.00000837591,0.0001213948,0.0001275394,0.0001144547,0.0001577287],"domain_scores_gemma":[0.9993963,0.00004786114,0.00001001976,0.0004458433,0.00004076889,0.00005917199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009022214,0.000674321,0.0004036,0.0001243214,0.001061951,0.00002709099,0.0008224082,0.03090123,0.03411212,0.008757123,0.008358201,0.9146674],"study_design_scores_gemma":[0.0007777606,0.0009689483,0.001759036,0.0001107331,0.0003206714,0.00003293103,0.0001046253,0.06571318,0.537733,0.1641193,0.226637,0.001722881],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007855516,0.00003353061,0.9887734,0.0001951797,0.0001316787,0.00005769927,0.000006378085,0.001146967,0.001799672],"genre_scores_gemma":[0.9726622,0.0002137529,0.02670854,0.0002460532,0.00004246906,0.0000234833,0.000001707985,0.00002746623,0.00007435778],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9648067,"threshold_uncertainty_score":0.4325932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01316880082188389,"score_gpt":0.2554402378666854,"score_spread":0.2422714370448015,"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."}}