{"id":"W2175858202","doi":"10.1007/s10044-015-0494-y","title":"A biologically inspired framework for contour detection","year":2015,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision","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":[0.0002259746,0.00006261568,0.0001261756,0.0001095547,0.00007799057,0.00009529354,0.0002283173,0.00005059562,0.0000080476],"category_scores_gemma":[0.00006440814,0.0000511643,0.00006463046,0.0005632084,0.00003355729,0.00008736714,0.00005622306,0.0000489352,0.000008508363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001594258,"about_ca_system_score_gemma":0.00001060306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005774952,"about_ca_topic_score_gemma":0.00003317964,"domain_scores_codex":[0.9993542,0.00002903491,0.0001665064,0.0002529224,0.0001019624,0.00009535087],"domain_scores_gemma":[0.9993157,0.00009852043,0.0000851154,0.0002646553,0.0001117307,0.0001242698],"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":[0.000001304846,0.00004696953,0.003574867,0.000004291493,0.00009103711,1.76056e-7,0.00009699698,0.000003885094,0.001312002,0.005481867,0.00009480273,0.9892918],"study_design_scores_gemma":[0.001954554,0.0006586679,0.0803435,0.00003576772,0.001367244,0.00001023277,0.000556463,0.3182828,0.1470525,0.4203304,0.02801534,0.001392602],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007404609,0.00003850629,0.9980201,0.0006987035,0.00001111961,0.0002745813,0.000006538653,0.00014555,0.00006442364],"genre_scores_gemma":[0.7428777,0.00001599023,0.255161,0.001109057,0.00005382072,0.0007418012,0.00001559069,0.000002619946,0.00002238406],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9878992,"threshold_uncertainty_score":0.208642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04008665302426928,"score_gpt":0.3272895018711384,"score_spread":0.2872028488468691,"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."}}