{"id":"W2901240113","doi":"10.1109/cvpr.2019.01046","title":"Divergence Prior and Vessel-Tree Reconstruction","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; University of Waterloo; Western University","funders":"Robarts Research Institute","keywords":"Regularization (linguistics); Divergence (linguistics); Curvature; Artificial intelligence; Computer science; Mathematics; Vector field; Algorithm; Computer vision; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001915923,0.0005328686,0.0008169634,0.001243402,0.000444921,0.001156786,0.001089458,0.001516809,0.001082462],"category_scores_gemma":[0.005845863,0.0006155141,0.0006542802,0.0007581533,0.001617531,0.001788904,0.00172631,0.002292855,0.0004871199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008317485,"about_ca_system_score_gemma":0.001027464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001550081,"about_ca_topic_score_gemma":0.001698987,"domain_scores_codex":[0.9992216,0.0002067862,0.00003137518,0.0001290846,0.0003627671,0.00004851029],"domain_scores_gemma":[0.9982967,0.0008569005,0.0002015843,0.0002255042,0.0003062748,0.0001129633],"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":[0.000162873,0.00008289261,0.001816031,0.0002124259,0.00007210921,0.0002403247,0.0002358651,0.5072734,0.06968063,0.2204899,0.003642056,0.1960915],"study_design_scores_gemma":[0.00001087477,0.00002875974,0.000402949,0.00001750296,0.000005545709,0.0001466578,0.00001075521,0.9347655,0.008200698,0.05407877,0.002305245,0.00002674727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002845056,0.00003472066,0.9966054,0.0000661892,0.000005824575,0.000005523376,0.00001572457,0.00009672472,0.0003248351],"genre_scores_gemma":[0.2180922,0.0002766295,0.7782415,0.0001806384,0.00007073261,0.0000848925,0.0003077901,0.0003353383,0.002410213],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001915923,"threshold_uncertainty_score":0.01013243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0196072834531498,"score_gpt":0.2778272156785668,"score_spread":0.2582199322254169,"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."}}