{"id":"W7097055603","doi":"","title":"Author manuscript, published in "International Conference on Image Analysis and Recognition, Canada (2007)" Automatic closed edge detection using level lines","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Edge detection; Enhanced Data Rates for GSM Evolution; Image (mathematics); Line (geometry); Pattern recognition (psychology); Probabilistic logic; Set (abstract data type); Level set (data structures); Flexibility (engineering)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002331253,0.0009296356,0.001666438,0.00239,0.001283001,0.00466673,0.001397124,0.00159098,0.2536176],"category_scores_gemma":[0.006541105,0.0005355176,0.0006131536,0.002041637,0.001382911,0.002383024,0.001276066,0.0009913992,0.09423818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001208376,"about_ca_system_score_gemma":0.001742114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008132175,"about_ca_topic_score_gemma":0.01559656,"domain_scores_codex":[0.9989005,0.0001512834,0.00009656132,0.0002861888,0.0004638282,0.0001015884],"domain_scores_gemma":[0.9960423,0.0005685865,0.000102494,0.0005198302,0.002470072,0.0002967926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004523851,0.00009139611,0.0009152439,0.0004163321,0.00009866362,0.0003876238,0.0001384048,0.00189756,0.01019369,0.006972663,0.5178998,0.4605362],"study_design_scores_gemma":[0.00009057832,0.0002639417,0.002572323,0.0002220239,0.00006349358,0.0009655865,0.0002769567,0.03222699,0.01817827,0.006188833,0.9388649,0.00008597918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01930951,0.01909247,0.5475044,0.01584972,0.1366863,0.001029657,0.006247046,0.01550887,0.2387719],"genre_scores_gemma":[0.03672829,0.005788264,0.1491748,0.001283622,0.004002983,0.0001307881,0.003908802,0.002651463,0.796331],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2536176,"threshold_uncertainty_score":0.8484353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09808730649235839,"score_gpt":0.3300848210611581,"score_spread":0.2319975145687997,"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."}}