{"id":"W4315436439","doi":"10.1242/dev.201008","title":"GliaMorph: a modular image analysis toolkit to quantify Müller glial cell morphology","year":2023,"lang":"en","type":"article","venue":"Development","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Biotechnology and Biological Sciences Research Council; Canadian Institutes of Health Research; Canada Foundation for Innovation; Wellcome Trust; Moorfields Eye Charity; University College London","keywords":"Biology; Segmentation; Modular design; Retina; Cell biology; Cell; Zebrafish; Phenotype; Artificial intelligence; Feature (linguistics); Neuroscience; Pattern recognition (psychology); Computational biology; Computer science; Biochemistry; Gene","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00042264,0.0002483139,0.0003297707,0.0004940461,0.0001080273,0.00005378028,0.0003901136,0.0001655193,0.0002105449],"category_scores_gemma":[0.000068818,0.0002535404,0.0002353687,0.001322401,0.00004450712,0.00000535137,0.0004737767,0.00009280587,0.0008655962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004521491,"about_ca_system_score_gemma":0.0001037934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004448659,"about_ca_topic_score_gemma":0.00008459684,"domain_scores_codex":[0.9981019,0.00006247235,0.0003921575,0.0007210617,0.0002560991,0.0004663403],"domain_scores_gemma":[0.9989271,0.00001059729,0.00008331219,0.0006731746,0.0001507167,0.0001550859],"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":[0.00003243207,0.00006652611,0.002623179,0.000009893025,0.0004579824,0.00007731139,0.0001519403,0.0001840122,0.9525306,0.0000063455,0.04247919,0.001380617],"study_design_scores_gemma":[0.0001961671,0.00005838864,0.01473597,0.000002807377,0.0001444078,0.000002929939,0.00006026253,0.0001779916,0.8186113,0.00001230617,0.165661,0.000336526],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664182,0.00005903397,0.03040794,0.0002464034,0.00005045971,0.000236445,0.000008347552,0.0001315213,0.002441653],"genre_scores_gemma":[0.9503293,0.00007280838,0.03718425,0.000716724,0.0001040965,0.0001484469,0.0009280084,0.00004475474,0.01047159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1339193,"threshold_uncertainty_score":0.9999917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0112010966662805,"score_gpt":0.2724959295115514,"score_spread":0.2612948328452709,"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."}}