{"id":"W2187548265","doi":"10.1109/iemcon.2015.7344424","title":"A curvature-based edge detector for x-ray radiographs","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Radiography; Detector; Curvature; Enhanced Data Rates for GSM Evolution; Computer vision; Computer science; Edge detection; Artificial intelligence; Object (grammar); Image (mathematics); Mathematics; Image processing; Medicine; Radiology; 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":[0.0008860463,0.0008010609,0.001269378,0.002502009,0.0005688759,0.001024324,0.001611412,0.001786286,0.001824338],"category_scores_gemma":[0.002170031,0.0008502622,0.001019227,0.001729284,0.0005231705,0.001268421,0.0009631431,0.00128739,0.001714671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004247536,"about_ca_system_score_gemma":0.0008080524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001683325,"about_ca_topic_score_gemma":0.002243575,"domain_scores_codex":[0.9988018,0.0001394664,0.00007501532,0.0001820212,0.0007112797,0.00009035286],"domain_scores_gemma":[0.9985878,0.0003410185,0.0001167788,0.0001437303,0.0007227112,0.00008792509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000521373,0.0001733663,0.002117842,0.0002959404,0.0001031692,0.0004560775,0.00008829754,0.01866358,0.3580846,0.004546607,0.005417765,0.6095312],"study_design_scores_gemma":[0.00007395013,0.0003334572,0.00638063,0.00005114956,0.00008382957,0.002175287,0.00004413891,0.7384289,0.2341298,0.002316122,0.01578718,0.000195587],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007785676,0.0005202442,0.9892597,0.0000850722,0.00006893642,0.00007170352,0.00006866897,0.001609261,0.0005307668],"genre_scores_gemma":[0.07554708,0.0006147373,0.920683,0.0001394195,0.00006663532,0.0000674163,0.0002848091,0.0002546911,0.002342215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002502009,"threshold_uncertainty_score":0.006103039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04143934638681803,"score_gpt":0.3028865274589606,"score_spread":0.2614471810721425,"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."}}