{"id":"W3000736671","doi":"10.1101/2020.01.16.909291","title":"A bright and high-performance genetically encoded Ca <sup>2+</sup> indicator based on mNeonGreen fluorescent protein","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Japan Society for the Promotion of Science; Canadian Institutes of Health Research; University of Alberta","keywords":"Green fluorescent protein; Zebrafish; Aequorea victoria; Fluorescence; Chemistry; Biophysics; In vivo; Fluorescent protein; Mantle (geology); Cell biology; Biology; Biochemistry; Physics; Genetics; Gene; Optics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003128571,0.0008900762,0.0006513931,0.000203692,0.0001929747,0.0001532275,0.0008955211,0.0009999353,0.00002883168],"category_scores_gemma":[0.0002092482,0.0009253884,0.0001352425,0.0002478418,0.0003910977,0.00001374335,0.0008867693,0.0009610413,0.00004150501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001600183,"about_ca_system_score_gemma":0.0007641823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002714337,"about_ca_topic_score_gemma":6.399249e-7,"domain_scores_codex":[0.9960607,0.0001696633,0.0006215912,0.001913883,0.000475162,0.000758976],"domain_scores_gemma":[0.9971731,0.00001758811,0.000368691,0.001671232,0.0002685335,0.000500856],"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":[0.0002678192,0.0001488542,0.002079804,0.0004307054,0.00007904454,0.00004023887,0.000005726592,0.0005453517,0.9959062,0.00005087013,0.0004170777,0.00002825308],"study_design_scores_gemma":[0.0006811953,0.0004851919,0.01117354,0.000505361,0.00006120277,3.947574e-8,9.079459e-7,0.008877513,0.9741493,0.000002659217,0.003056363,0.001006746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732887,0.0005394166,0.02286613,0.0004628034,0.0001224102,0.001961196,0.0004589984,0.0002944378,0.000005970028],"genre_scores_gemma":[0.9030851,0.0003054655,0.09459052,0.0008276683,0.0004089441,0.0005683188,0.000008024325,0.0002016511,0.000004380287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07172439,"threshold_uncertainty_score":0.9993197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00663121039783361,"score_gpt":0.2142436492631666,"score_spread":0.207612438865333,"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."}}