{"id":"W2170551185","doi":"10.1186/1471-2105-12-237","title":"Semi-Automatic segmentation of multiple mouse embryos in MR images","year":2011,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Centre for Phenogenomics; Hospital for Sick Children","funders":"Genome Canada; Ontario Genomics; Institut national de recherche en informatique et en automatique (INRIA); British Heart Foundation; Wellcome Trust; Ontario Genomics Institute","keywords":"Initialization; Segmentation; Artificial intelligence; Computer science; Computer vision; Intersection (aeronautics); Image segmentation; Magnetic resonance imaging; Pattern recognition (psychology); Collision; Boundary (topology); Collision detection; Algorithm; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002411638,0.00009926855,0.0001187722,0.0001028318,0.00002521351,0.000009879021,0.0002331107,0.0001065593,0.0000525107],"category_scores_gemma":[0.0003691824,0.00009041384,0.00004980695,0.0001805448,0.0001322933,0.00001383296,0.0001140687,0.00007120596,0.00004440652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001808494,"about_ca_system_score_gemma":0.0001027699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000495256,"about_ca_topic_score_gemma":0.00008211631,"domain_scores_codex":[0.9990267,0.00002960789,0.0004250533,0.0001061234,0.0002012258,0.0002113235],"domain_scores_gemma":[0.9993326,0.00003310002,0.0001362282,0.0003326172,0.00007173145,0.0000937124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001093302,0.0006815452,0.04720997,0.001037598,0.00007243609,0.000001420699,0.001863875,0.00008004595,0.9218373,0.000167243,0.007377417,0.01956184],"study_design_scores_gemma":[0.001191783,0.0002069039,0.01729372,0.00003446511,0.00000996668,0.000004685995,0.001092648,0.05456225,0.9248039,0.0001190481,0.0004851139,0.0001955199],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8995814,0.00007188694,0.09652439,0.0000217414,0.00003001281,0.0006373605,0.00009272946,0.00002651305,0.003013992],"genre_scores_gemma":[0.8121724,0.00009769324,0.1868739,0.00009936041,0.00002536404,0.0001113998,0.0002952217,0.00001722529,0.0003074399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09034951,"threshold_uncertainty_score":0.368697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02431074920467926,"score_gpt":0.2803238978378402,"score_spread":0.2560131486331609,"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."}}