{"id":"W2131802064","doi":"10.1109/bibm.2009.69","title":"Biofilm Image Segmentation Using Optimal Multi-level Thresholding","year":2009,"lang":"en","type":"article","venue":"","topic":"Bacterial biofilms and quorum sensing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thresholding; Segmentation; Image segmentation; Artificial intelligence; Computer science; Cluster analysis; Process (computing); Biofilm; Pattern recognition (psychology); Scale-space segmentation; Image (mathematics); Computer vision; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001264242,0.0004513429,0.0007576733,0.001779944,0.0004706343,0.001343748,0.0008403571,0.001150095,0.001264707],"category_scores_gemma":[0.002780809,0.000523437,0.000880326,0.0008667079,0.0007501986,0.001060685,0.0008884942,0.0009345226,0.0005745339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007473048,"about_ca_system_score_gemma":0.0007993871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001268932,"about_ca_topic_score_gemma":0.001621232,"domain_scores_codex":[0.999028,0.0001819966,0.00007792703,0.0002252594,0.0003885724,0.00009834042],"domain_scores_gemma":[0.9991261,0.0003418529,0.000108874,0.0001198204,0.0002598889,0.0000434814],"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":[0.0002680401,0.00006457986,0.001276884,0.0003668565,0.00007500988,0.0001688888,0.0003089951,0.02250369,0.734933,0.004449481,0.0008245676,0.23476],"study_design_scores_gemma":[0.00003039494,0.0002165331,0.007097303,0.00007444336,0.00007866663,0.0007201778,0.0001552285,0.4984644,0.4803374,0.007762386,0.004932374,0.0001307742],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04584534,0.0004545791,0.9512826,0.0001769011,0.00003285074,0.00007254512,0.00005972786,0.0008114448,0.001263945],"genre_scores_gemma":[0.174957,0.0003007822,0.8236846,0.00004323986,0.00001421754,0.00006139823,0.0001002327,0.0001912526,0.0006471737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001779944,"threshold_uncertainty_score":0.006686032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04071700367773151,"score_gpt":0.2997543850116491,"score_spread":0.2590373813339176,"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."}}