{"id":"W4252599270","doi":"10.1111/cgf.12964","title":"Near‐Isometric Level Set Tracking","year":2016,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Isometric exercise; Set (abstract data type); Tracking (education); Computer graphics; Computer vision; Algorithm; Deformation (meteorology); Artificial intelligence","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":[0.0005578551,0.0003094091,0.0004159826,0.00061009,0.0003610134,0.0009309917,0.0008358692,0.0006797311,0.002409956],"category_scores_gemma":[0.002514437,0.0002568455,0.0003425019,0.0005019364,0.000718334,0.00113536,0.001513991,0.000948408,0.0005591266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006458455,"about_ca_system_score_gemma":0.0005756433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001688022,"about_ca_topic_score_gemma":0.001081213,"domain_scores_codex":[0.9997469,0.0000405166,0.00001644603,0.0000462218,0.0001242707,0.00002560705],"domain_scores_gemma":[0.9993493,0.0001701604,0.00007961551,0.0001971602,0.0001411589,0.00006266441],"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":[0.0001533937,0.00007502708,0.002103552,0.00006968799,0.00002566518,0.0001008036,0.000284033,0.6163408,0.0496136,0.1315291,0.002465954,0.1972384],"study_design_scores_gemma":[0.000003813169,0.00001476779,0.0001425982,0.000003392716,0.000001226826,0.00002207957,0.00001136505,0.9864625,0.003041079,0.009377658,0.0009144815,0.000005091491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02863158,0.00002293617,0.9691492,0.00007510403,0.00002688962,0.00002164319,0.00002531559,0.0004301006,0.001617218],"genre_scores_gemma":[0.558818,0.00008899228,0.4370859,0.00006443153,0.00002028923,0.00006790752,0.00015896,0.0001743112,0.003521128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002409956,"threshold_uncertainty_score":0.008062124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05975429444611798,"score_gpt":0.3001475216225661,"score_spread":0.2403932271764482,"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."}}