{"id":"W3015634290","doi":"10.1145/3386569.3392401","title":"PolyFit","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Raster graphics; Vectorization (mathematics); Computer science; Polygon (computer graphics); Piecewise; Set (abstract data type); Artificial intelligence; Segmentation; Computer vision; Algorithm; Mathematics","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":[0.0009460151,0.002074118,0.001379052,0.001542643,0.000971409,0.00355649,0.004594579,0.002200272,0.2674158],"category_scores_gemma":[0.004084202,0.001298326,0.002493601,0.001519073,0.0007406662,0.00309772,0.003696197,0.002805255,0.1204992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007574178,"about_ca_system_score_gemma":0.001528858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002958139,"about_ca_topic_score_gemma":0.00692601,"domain_scores_codex":[0.9989395,0.00009184527,0.00006703006,0.0002553242,0.0005340416,0.0001122082],"domain_scores_gemma":[0.9990479,0.0002411443,0.00005373364,0.0002793052,0.0002800379,0.00009792667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00067805,0.0002409093,0.00157959,0.001586209,0.0001934762,0.0003314315,0.0002494609,0.01817009,0.01076178,0.02703474,0.5275302,0.4116441],"study_design_scores_gemma":[0.0003365481,0.0002337824,0.001653942,0.0002487023,0.00008726475,0.0007889193,0.0001828463,0.1891676,0.01829525,0.0321883,0.7566122,0.000204718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006412944,0.001860762,0.5813616,0.0008528025,0.001369931,0.000669441,0.02158582,0.2648824,0.1210043],"genre_scores_gemma":[0.08888103,0.002483687,0.5669121,0.001612971,0.0003254226,0.001729118,0.09073801,0.08855511,0.1587626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2674158,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04562600105871609,"score_gpt":0.2721601140273099,"score_spread":0.2265341129685939,"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."}}