{"id":"W2161944241","doi":"10.1109/iembs.2011.6091482","title":"A novel approach to automated cell counting for studying human corneal epithelial cells","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Computer science; Segmentation; Artificial intelligence; Cluster analysis; Image segmentation; Computer vision; Lens (geology); Pattern recognition (psychology); Set (abstract data type); Sample (material); Cell counting; Cell; Image (mathematics); Biology; Chemistry; Chromatography","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":[0.000129204,0.0001231932,0.0001165386,0.00005810578,0.0001450813,0.00004931869,0.0001907403,0.00005162899,0.00001162333],"category_scores_gemma":[0.000002316943,0.0001208169,0.00003556986,0.0001357903,0.0000126976,0.00006830802,0.000036785,0.00006922163,0.00001780146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002716313,"about_ca_system_score_gemma":0.000007897418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004937953,"about_ca_topic_score_gemma":0.000001992542,"domain_scores_codex":[0.9993356,0.000002065154,0.0001829964,0.0001749802,0.00006524206,0.000239043],"domain_scores_gemma":[0.9996873,0.0000114073,0.00002510441,0.0001662483,0.00005714462,0.00005277375],"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":[0.000002940408,0.0001863312,0.00001326915,0.000153135,0.00001000832,1.505102e-7,0.0009519091,0.0006838047,0.9916891,0.001852783,0.003698854,0.0007577358],"study_design_scores_gemma":[0.0003133756,0.00003586852,0.0001131815,0.00001170491,0.00001996568,0.000001881294,0.0001576171,0.3570398,0.6401292,0.0002029029,0.001697318,0.000277156],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02944266,0.000005882356,0.9204409,0.000003520786,0.0000391254,0.0005510446,0.00001943999,0.003236272,0.04626119],"genre_scores_gemma":[0.5945501,3.263632e-7,0.4049402,0.0000372697,0.00003273673,0.0002133248,0.000005998823,0.00003126125,0.0001887594],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5651074,"threshold_uncertainty_score":0.492677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04975686118725336,"score_gpt":0.2610023762623948,"score_spread":0.2112455150751414,"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."}}