{"id":"W4286210115","doi":"10.1101/2022.07.19.500542","title":"MiniVess: A dataset of rodent cerebrovasculature from <i>in vivo</i> multiphoton fluorescence microscopy imaging","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Japan Agency for Medical Research and Development","keywords":"Generalizability theory; Artificial intelligence; Computer science; Preprocessor; Reliability (semiconductor); Image (mathematics); Pattern recognition (psychology); Machine learning; Computer vision; Psychology; Physics","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":[0.0008512686,0.001780726,0.001303358,0.002449528,0.0006108126,0.001291128,0.002267934,0.001684478,0.003713725],"category_scores_gemma":[0.00175286,0.0006673199,0.001474785,0.002091718,0.0005922285,0.0006653639,0.001348559,0.0008914465,0.003805472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007774879,"about_ca_system_score_gemma":0.001247504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007531367,"about_ca_topic_score_gemma":0.01840773,"domain_scores_codex":[0.9993345,0.00007609411,0.00006406477,0.0002279418,0.0002017798,0.00009550877],"domain_scores_gemma":[0.9990259,0.0001791793,0.0001184872,0.0003236637,0.0002560852,0.00009663848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002375429,0.0006774663,0.02439144,0.00760833,0.001411818,0.002770037,0.000419832,0.03695647,0.2372271,0.003577036,0.4925405,0.1900446],"study_design_scores_gemma":[0.0007121394,0.000936762,0.1526658,0.001013834,0.0008820936,0.01093096,0.0005776719,0.1038731,0.2094008,0.01465761,0.5037346,0.0006148359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2107231,0.007309691,0.04722112,0.0008368222,0.0004108906,0.0004475216,0.6960633,0.03009612,0.006891401],"genre_scores_gemma":[0.07897119,0.001341269,0.04157218,0.000266392,0.00004640811,0.0004672916,0.8741146,0.001507739,0.0017129],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007531367,"threshold_uncertainty_score":0.01497507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005442342637536536,"score_gpt":0.2337613258377683,"score_spread":0.2283189832002318,"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."}}