{"id":"W2122266115","doi":"10.1109/iembs.1995.575170","title":"A method to match human sulci in 3D-space","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Noise (video); Deformation (meteorology); Pattern recognition (psychology); Transformation (genetics); Space (punctuation); Image (mathematics); Geology; Biology","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":[0.000912226,0.001117685,0.0006554305,0.002061814,0.0007266925,0.001075181,0.001173763,0.001438863,0.0030429],"category_scores_gemma":[0.002175851,0.0007906454,0.001067808,0.001551713,0.000746099,0.001151075,0.001243694,0.0009314265,0.002179902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000580672,"about_ca_system_score_gemma":0.001406594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003837582,"about_ca_topic_score_gemma":0.005351806,"domain_scores_codex":[0.999437,0.00006812565,0.00003254732,0.0001340285,0.0002931709,0.00003504274],"domain_scores_gemma":[0.9994186,0.0001374042,0.00006483622,0.0001836524,0.0001569805,0.00003845084],"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":[0.0001004454,0.0000580175,0.00104113,0.0002173892,0.0001679481,0.0001530718,0.000188995,0.03261438,0.06137412,0.01278629,0.008405059,0.8828931],"study_design_scores_gemma":[0.000108442,0.0002701987,0.003691344,0.00006264223,0.0001153656,0.003271623,0.000120083,0.792274,0.09371072,0.0241263,0.08206346,0.0001858447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001646358,0.0001059896,0.9965834,0.0000685099,0.00004144737,0.00005120674,0.00005208401,0.001057463,0.0003936229],"genre_scores_gemma":[0.01794144,0.0001210276,0.9804155,0.00004347464,0.00002269199,0.00008749482,0.0001679264,0.0002037982,0.0009965262],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003837582,"threshold_uncertainty_score":0.01017952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03897189740719519,"score_gpt":0.35880162236295,"score_spread":0.3198297249557548,"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."}}