{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006189785,0.00009180868,0.0000776851,0.000112343,0.0002039327,0.00007127439,0.0005837805,0.00005631829,0.00005688477],"category_scores_gemma":[0.00001786527,0.00008946024,0.0001180428,0.0009363827,0.0000281196,0.0002822504,0.000007515116,0.0002149085,0.0002102872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000870035,"about_ca_system_score_gemma":0.00001738241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007136558,"about_ca_topic_score_gemma":0.000006488271,"domain_scores_codex":[0.9991965,0.00003599616,0.000135722,0.0002686578,0.000219727,0.0001433902],"domain_scores_gemma":[0.9993568,0.00003541273,0.00002864737,0.0004058052,0.00003937759,0.0001339533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009944595,0.0009337602,0.0004607918,0.00007325515,0.0001901558,0.00004460842,0.004258348,0.00191631,0.01252478,0.2282288,0.003027064,0.7482427],"study_design_scores_gemma":[0.005693802,0.006776528,0.02088625,0.0001124305,0.0001818868,0.0001775185,0.0007648993,0.4767421,0.1190882,0.1285198,0.2376449,0.003411676],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004718699,0.00001468241,0.9759104,0.01810482,0.0003328799,0.00007133918,0.000002663546,0.0004135988,0.0004309087],"genre_scores_gemma":[0.9869925,0.00004030215,0.006123951,0.00671452,0.00002812559,0.0000102344,6.843274e-7,0.000007113762,0.00008260254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9822738,"threshold_uncertainty_score":0.3648083,"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."}}