{"id":"W2003436630","doi":"10.1016/j.neunet.2005.06.032","title":"Refining competition in the self-organising tree map for unsupervised biofilm image segmentation","year":2005,"lang":"en","type":"article","venue":"Neural Networks","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Pattern recognition (psychology); Context (archaeology); Pixel; Image segmentation; Market segmentation; Computer vision; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002540698,0.0001128681,0.00009631593,0.00003703465,0.0000837244,0.00006185319,0.0001585032,0.00009131653,0.00001173458],"category_scores_gemma":[0.00001414472,0.0000938068,0.00007925374,0.0001124241,0.00002452184,0.00001237187,0.00003489535,0.00009804578,0.000002091517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002060084,"about_ca_system_score_gemma":0.000007753648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001038786,"about_ca_topic_score_gemma":0.0001958828,"domain_scores_codex":[0.9992168,0.00009078954,0.0001911491,0.0002315343,0.00007845817,0.0001912552],"domain_scores_gemma":[0.9996201,0.0000310152,0.00006405867,0.0002229547,0.00004168243,0.0000201395],"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":[0.00005488309,0.00008342236,0.001036199,0.00001303341,0.00002330569,0.000002965712,0.00009938872,0.001549576,0.9570087,0.00003643595,0.006709614,0.03338244],"study_design_scores_gemma":[0.001903242,0.0005009766,0.003202931,0.00003575767,0.0001373495,0.00002771191,0.000362405,0.2983808,0.6650024,0.00006752391,0.02981792,0.0005610012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9272913,0.0006305295,0.0680513,0.001996074,0.00006447475,0.00054929,0.000004257635,0.0001071987,0.001305519],"genre_scores_gemma":[0.9801604,0.0000698308,0.01733333,0.001409322,0.000469543,0.00004730779,0.0004244975,0.00001984607,0.0000659845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2968312,"threshold_uncertainty_score":0.382533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008764584474508147,"score_gpt":0.2603359586101857,"score_spread":0.2515713741356775,"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."}}