{"id":"W2001586684","doi":"10.1109/icip.2011.6116627","title":"Clump splitting via bottleneck detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bottleneck; Image segmentation; Line (geometry); Segmentation; Regular polygon; Image (mathematics); Computer science; Algorithm; Computer vision; Artificial intelligence; Line segment; Mathematics; Geometry","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":[0.002590102,0.001314544,0.002210644,0.003909864,0.001191566,0.001870974,0.002781557,0.001747716,0.00160824],"category_scores_gemma":[0.005775642,0.001048916,0.001185359,0.001741923,0.001390482,0.002946565,0.002905773,0.001346366,0.001065828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008989302,"about_ca_system_score_gemma":0.001113948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001328216,"about_ca_topic_score_gemma":0.001265314,"domain_scores_codex":[0.9980628,0.0002510498,0.0001358296,0.0005114439,0.0008251437,0.0002136757],"domain_scores_gemma":[0.9955232,0.001549636,0.0007418171,0.0006831668,0.001247051,0.0002550895],"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":[0.001020259,0.0002398604,0.007515625,0.000480017,0.0002409561,0.001186488,0.000977029,0.08244296,0.2107641,0.01234262,0.004807626,0.6779824],"study_design_scores_gemma":[0.00004255033,0.0001801364,0.00218369,0.00003764211,0.00006879878,0.0009776246,0.0001783491,0.8708237,0.105793,0.01417493,0.005459956,0.00007963144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02563795,0.0002409916,0.9723805,0.00006961972,0.00003871936,0.00007853591,0.00003029828,0.001011511,0.0005119505],"genre_scores_gemma":[0.2551252,0.0002689839,0.7418213,0.0001138821,0.00007426557,0.0001368056,0.0002739193,0.0003930192,0.001792597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003909864,"threshold_uncertainty_score":0.01369792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02966398970413116,"score_gpt":0.2439320508970202,"score_spread":0.2142680611928891,"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."}}