{"id":"W4304123577","doi":"10.7554/elife.77419","title":"EZ Clear for simple, rapid, and robust mouse whole organ clearing","year":2022,"lang":"en","type":"article","venue":"eLife","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Canadian Institutes of Health Research; National Institutes of Health; Cancer Prevention and Research Institute of Texas; National Institute of General Medical Sciences; American Heart Association; National Heart, Lung, and Blood Institute; U.S. Department of Defense","keywords":"Clearing; Fluorescence; Computer science; Biomedical engineering; Biology; Computational biology; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001467706,0.0001008178,0.00009509479,0.00002709482,0.0002370707,0.00001898605,0.0001543761,0.00005186745,0.00002888726],"category_scores_gemma":[0.00006220234,0.0001132555,0.00003758063,0.00005225294,0.00005114721,0.00000394614,0.0003330747,0.0001019567,0.000002115494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001850885,"about_ca_system_score_gemma":0.00002576045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006845653,"about_ca_topic_score_gemma":0.00000579444,"domain_scores_codex":[0.999272,0.00002529986,0.0001182696,0.0002909815,0.00008544411,0.0002080317],"domain_scores_gemma":[0.9996186,0.000007551967,0.00004590568,0.0002372588,0.00003673421,0.00005392848],"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":[0.00009480823,0.00002306554,0.0005739856,0.00000941741,0.000009778038,9.381126e-7,0.00003958097,0.0001947402,0.9845978,0.00005090381,0.01179772,0.002607235],"study_design_scores_gemma":[0.0002796365,0.0003437643,0.0001232949,0.000001668146,0.000004598833,0.000006319267,0.0001692851,0.0001374283,0.7285755,0.00003601658,0.2701911,0.0001313964],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9506205,0.0009058872,0.04717181,0.0002641482,0.00008143296,0.000514636,0.0001426708,0.0001094791,0.0001894546],"genre_scores_gemma":[0.9077233,0.0003049755,0.08728928,0.001778664,0.0002705819,0.0001890789,0.0003589581,0.0001106488,0.001974522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2583934,"threshold_uncertainty_score":0.4618424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01198586117746733,"score_gpt":0.2598389047146752,"score_spread":0.2478530435372079,"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."}}