{"id":"W2024351211","doi":"10.1016/s1077-3142(03)00099-7","title":"Object-level structured contour map extraction","year":2003,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Curvature; Artificial intelligence; Computer vision; Graph; Active contour model; Computer science; Mathematics; Contour line; Object (grammar); Process (computing); Pattern recognition (psychology); Algorithm; Topology (electrical circuits); Image (mathematics); Geometry; Image segmentation; Theoretical computer science; Combinatorics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003833997,0.0001790082,0.0001813758,0.0001829582,0.0002475146,0.0006097588,0.0002613582,0.00007456865,0.00009622984],"category_scores_gemma":[0.0000432309,0.000156249,0.00005066677,0.0002003028,0.0000954246,0.001165296,0.0001289233,0.0001846085,0.00001665697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001508602,"about_ca_system_score_gemma":0.0000369737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004079534,"about_ca_topic_score_gemma":0.000001952687,"domain_scores_codex":[0.998568,0.0001480789,0.0002678751,0.000437986,0.000320178,0.0002578622],"domain_scores_gemma":[0.9992121,0.0001353069,0.0001089836,0.0003014482,0.00005941105,0.0001827779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003837572,0.0002207272,0.0002280617,0.0001940191,0.00008876376,0.0002830715,0.002870787,0.000008278062,0.1746806,0.348001,0.1030461,0.3703402],"study_design_scores_gemma":[0.009157887,0.001894922,0.005096238,0.0008138322,0.00007033585,0.001286351,0.002627558,0.2591289,0.3488193,0.3522561,0.01575973,0.00308886],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003179648,0.00008657981,0.9968739,0.0003445497,0.0006866913,0.0001895506,0.000001477348,0.0003029088,0.001196333],"genre_scores_gemma":[0.2844523,0.00003367961,0.7144768,0.0008085647,0.00005873476,0.000003443293,0.000003190359,0.00001129743,0.0001518809],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3672513,"threshold_uncertainty_score":0.637165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05317088986267293,"score_gpt":0.3200865482859827,"score_spread":0.2669156584233097,"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."}}