{"id":"W2020380901","doi":"10.1016/j.jneumeth.2010.12.018","title":"Automated and quantitative image analysis of ischemic dendritic blebbing using in vivo 2-photon microscopy data","year":2010,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Neuroinflammation and Neurodegeneration Mechanisms","field":"Neuroscience","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of British Columbia and Yukon; Heart and Stroke Foundation of Canada","keywords":"Ischemia; In vivo; Pathology; Bleb (medicine); Biology; Biomedical engineering; Computer science; Medicine; Neuroscience; Glaucoma; Cardiology","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":[0.002464853,0.000173733,0.0004954702,0.001233824,0.000141243,0.0001567781,0.0008231042,0.00005893342,0.00002603492],"category_scores_gemma":[0.006515447,0.0001564204,0.0000928271,0.002645053,0.0003089752,0.001209284,0.0002332378,0.0004800785,3.845439e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001529768,"about_ca_system_score_gemma":0.0001453766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007058982,"about_ca_topic_score_gemma":0.00001182989,"domain_scores_codex":[0.9969482,0.0008629845,0.0009237166,0.000524085,0.0004775006,0.0002635562],"domain_scores_gemma":[0.9973897,0.000923247,0.0009004705,0.0005005633,0.0001440239,0.000142017],"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":[0.00002678536,0.0000550922,0.0001798518,0.00001268313,0.000003767826,0.00005792833,0.0001489467,0.0004556934,0.9986929,0.0002305924,0.0000208951,0.0001148669],"study_design_scores_gemma":[0.000184275,0.00005563352,0.0005305549,0.00001193818,0.00007412779,0.0001778269,0.00002518518,0.387238,0.6114779,0.00003317759,0.0001143611,0.00007713296],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527295,0.00001390828,0.04610496,0.000118641,0.0008121652,0.0001201791,0.00002998972,0.00002846294,0.00004219087],"genre_scores_gemma":[0.6571684,0.00007345872,0.342277,0.0004183322,0.00001943551,8.508132e-7,4.240971e-7,0.00001808889,0.00002405923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3872151,"threshold_uncertainty_score":0.7800069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1372322074082982,"score_gpt":0.4636026278698872,"score_spread":0.3263704204615889,"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."}}