{"id":"W3166644885","doi":"10.1101/2021.05.31.446382","title":"Micro-Meta App: an interactive software tool to facilitate the collection of microscopy metadata based on community-driven specifications","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Neurological Disorders and Stroke; RIKEN; Intellectual and Developmental Disabilities Research Center; 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 Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Metadata; Computer science; Interoperability; Software; Documentation; Virtual microscopy; Context (archaeology); Interface (matter); Data collection; World Wide Web; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008364178,0.0004619645,0.0005998649,0.0002453022,0.0003028533,0.0002729009,0.001146686,0.0003336448,0.00003450023],"category_scores_gemma":[0.0006264555,0.0004257673,0.0003936797,0.0005251015,0.0001613495,0.00002673061,0.0008009556,0.0007918971,0.000006554438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001285454,"about_ca_system_score_gemma":0.0004449815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003641942,"about_ca_topic_score_gemma":0.00009154594,"domain_scores_codex":[0.9971753,0.0008922401,0.0005431931,0.000858738,0.0002543591,0.0002761394],"domain_scores_gemma":[0.9945598,0.0001098912,0.0004178323,0.00378845,0.001007068,0.0001169896],"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":[0.0001124402,0.0003717579,0.000297641,0.00008261081,0.0008659863,0.000002946121,0.00003414255,0.0003693247,0.9963043,0.000003928357,0.001551014,0.00000395083],"study_design_scores_gemma":[0.000179442,0.0001925908,0.005177632,0.00008233116,0.0006979263,1.727123e-8,0.00003215182,0.0001575764,0.9842749,3.516609e-7,0.008785014,0.000420047],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9030342,0.000294451,0.09430482,0.0001948626,0.0001224819,0.001078824,0.0008340861,0.0001246737,0.00001160818],"genre_scores_gemma":[0.9428483,0.0001455723,0.05567339,0.0006627705,0.00008139274,0.0004136681,0.0000461168,0.00008200959,0.00004672894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03981416,"threshold_uncertainty_score":0.9998194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04088450636366768,"score_gpt":0.2711069386525796,"score_spread":0.2302224322889119,"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."}}