{"id":"W4226251227","doi":"10.1038/s41592-021-01327-9","title":"Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model","year":2021,"lang":"en","type":"article","venue":"Nature Methods","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Institute of Biomedical Imaging and Bioengineering; Fogarty International Center; National Cancer Institute; RIKEN; National Institute of Standards and Technology; National Institute on Drug Abuse; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Agence Nationale de la Recherche; Deutsche Forschungsgemeinschaft; Infrastructures en Biologie Santé et Agronomie; Chan Zuckerberg Initiative; Silicon Valley Community Foundation; National Institutes of Health; National Science Foundation","keywords":"Metadata; Computer science; Microscopy; Scale (ratio); Data quality; Quality (philosophy); Data science; Data mining; World Wide Web; Cartography; Engineering; Medicine; Pathology; Physics","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":[0.002415001,0.0001836281,0.0002587526,0.00005764272,0.0004099369,0.0001454534,0.0006735562,0.0003583384,0.00001896035],"category_scores_gemma":[0.001409963,0.0001422554,0.0002698624,0.0002663199,0.00008292633,0.00001273033,0.0003771182,0.0006909734,4.509172e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005446317,"about_ca_system_score_gemma":0.0002858929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000077003,"about_ca_topic_score_gemma":0.00004764121,"domain_scores_codex":[0.9981255,0.0008464282,0.0002498468,0.0003561816,0.0001923627,0.0002297052],"domain_scores_gemma":[0.9974729,0.0001188967,0.000112332,0.001610822,0.0006288252,0.00005619674],"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":[0.00002369208,0.00005448316,0.000007538833,0.00001886629,0.0001673223,8.771534e-7,0.00007566188,0.00003458724,0.9605789,0.001012706,0.02882572,0.009199616],"study_design_scores_gemma":[0.0001131917,0.00002315826,0.00002170121,0.000006716485,0.0001160523,0.000006273966,0.00008772605,0.0009097765,0.6285167,0.001468556,0.3686177,0.0001124383],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002766546,0.008796465,0.9828259,0.001317675,0.00008056973,0.0002661796,0.0002468907,0.00003684802,0.003662881],"genre_scores_gemma":[0.05226194,0.0009740596,0.9404389,0.0009833361,0.0001904466,0.000089967,0.0008987162,0.0000454183,0.004117243],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.339792,"threshold_uncertainty_score":0.5801005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05095567350507166,"score_gpt":0.4397060971681553,"score_spread":0.3887504236630837,"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."}}