{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005038555,0.0003237194,0.0002964458,0.001038653,0.0003884218,0.0006639842,0.001967119,0.0001616768,0.00000851257],"category_scores_gemma":[0.00002950868,0.0002468532,0.0002570819,0.002837677,0.0001472001,0.0007619153,0.001041043,0.0001700861,0.00003499057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003511616,"about_ca_system_score_gemma":0.00008940796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001333246,"about_ca_topic_score_gemma":0.00001034833,"domain_scores_codex":[0.9974418,0.0001031457,0.0004812246,0.0007921358,0.0005253134,0.0006563981],"domain_scores_gemma":[0.9978732,0.0002235903,0.0001930384,0.001164244,0.0003252866,0.0002205887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000198039,0.00005901494,0.009549858,0.000009284276,0.00002781602,0.000009623833,0.000110324,0.000002319976,0.00003751802,0.8550969,0.01091221,0.1241832],"study_design_scores_gemma":[0.001867857,0.001015247,0.04752268,0.0003214514,0.00001991946,0.0001570173,0.000007742889,0.4952207,0.004212619,0.2546031,0.1932354,0.00181628],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005998716,0.0001655792,0.9899694,0.001603179,0.000968502,0.0002276812,0.00001386818,0.0009478347,0.0001052813],"genre_scores_gemma":[0.9245034,0.0001859177,0.07270215,0.002262373,0.0001892245,0.00002422092,0.000007167278,0.00003974032,0.00008581371],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9185047,"threshold_uncertainty_score":0.9999984,"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."}}