{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.06800008,0.001557898,0.002455651,0.007865688,0.004038078,0.01833654,0.009516587,0.008349412,0.005031032],"category_scores_gemma":[0.08229052,0.002768731,0.004413207,0.00640688,0.005593486,0.02606513,0.02197782,0.01100187,0.005446684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005138087,"about_ca_system_score_gemma":0.01471172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01648107,"about_ca_topic_score_gemma":0.02132972,"domain_scores_codex":[0.9533558,0.01029199,0.01348134,0.003608467,0.01533405,0.003928349],"domain_scores_gemma":[0.8398229,0.01814473,0.007715449,0.07600383,0.04988072,0.008432385],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006611951,0.0007045781,0.007004738,0.001084405,0.0002627113,0.0009202879,0.003219803,0.0142588,0.01894814,0.8000647,0.03166319,0.1212075],"study_design_scores_gemma":[0.0001611494,0.0002766851,0.001978987,0.001567165,0.0003355558,0.0008304818,0.001892023,0.1130385,0.03249991,0.5225931,0.3242519,0.0005743879],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006117282,0.0002725492,0.9750071,0.001935141,0.0003226115,0.0007145664,0.001504357,0.008502134,0.005624326],"genre_scores_gemma":[0.09599246,0.0007534331,0.8758966,0.001862809,0.0003151184,0.001370624,0.009803608,0.005068997,0.008936274],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9816635,"threshold_uncertainty_score":0.3596231,"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."}}