{"id":"W2912696013","doi":"10.1016/j.media.2005.09.002","title":"United Snakes","year":2005,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Turun Yliopisto","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Segmentation; Computer vision; Image segmentation; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000827031,0.0001354086,0.0002747705,0.0007705534,0.00008504134,0.0001598743,0.001282065,0.0000980287,0.0051257],"category_scores_gemma":[0.0008415066,0.0001100819,0.0002139743,0.003894865,0.0001811983,0.0006439314,0.0002495116,0.0002424469,0.0004506046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004584901,"about_ca_system_score_gemma":0.00006729562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007223414,"about_ca_topic_score_gemma":0.00003887963,"domain_scores_codex":[0.9974465,0.0001590913,0.0004069713,0.0003940322,0.001291123,0.0003023102],"domain_scores_gemma":[0.9984307,0.0001752541,0.00008861844,0.0006663175,0.0001472924,0.0004918369],"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":[0.000002561711,0.0003028958,0.000869891,0.00001111048,0.0007108242,0.0002269671,0.0004750284,0.0000198204,0.001732593,0.002546798,0.05511745,0.937984],"study_design_scores_gemma":[0.0007774418,0.00007709063,0.002251507,0.00002834649,0.0006550457,0.00002713513,0.00007340756,0.8747053,0.068011,0.0008226946,0.05194847,0.000622594],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001086222,0.00006362017,0.9820455,0.01284052,0.00003588022,0.00005792881,0.000001308367,0.0005410684,0.003327938],"genre_scores_gemma":[0.1077731,0.0001240339,0.8695725,0.02006673,0.0002719342,0.00003502889,0.00007042454,0.00001385138,0.002072415],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9373615,"threshold_uncertainty_score":0.9957837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01048284823935208,"score_gpt":0.3051294323258833,"score_spread":0.2946465840865312,"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."}}