{"id":"W4407693094","doi":"10.1021/acsnano.4c14791","title":"Nanoscale Resolution Imaging of Whole Mouse Embryos Using Expansion Microscopy","year":2025,"lang":"en","type":"article","venue":"ACS Nano","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health; National Research Foundation of Korea; Korea Health Industry Development Institute; Samsung; Howard Hughes Medical Institute; Good Ventures Foundation; National Institute of Biomedical Imaging and Bioengineering; Korea Institute of Science and Technology; Open Philanthropy Project","keywords":"Nanoscopic scale; Microscopy; Materials science; Resolution (logic); Nanotechnology; Optics; Computer science; Artificial intelligence; 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":[0.00006052933,0.0000958207,0.00009857122,0.00004923042,0.0001058125,0.000009049541,0.000137596,0.00007683942,0.000002200459],"category_scores_gemma":[0.00001512008,0.000101531,0.00004712879,0.0001480868,0.00006976278,0.000005158824,0.00009644918,0.00005234353,0.000001535489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000259198,"about_ca_system_score_gemma":0.00006970374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000285266,"about_ca_topic_score_gemma":0.000004886462,"domain_scores_codex":[0.9993504,0.00001718656,0.0001748399,0.0002359602,0.00004544745,0.0001761553],"domain_scores_gemma":[0.9994924,0.000003964394,0.00007385165,0.0003349194,0.00007429039,0.00002056312],"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":[0.00002249609,0.00003841175,0.0004140659,0.00001261634,0.000005982608,1.074159e-7,0.00000633333,0.00005623237,0.9958872,0.0001585847,0.002597179,0.000800847],"study_design_scores_gemma":[0.0001761125,0.00003215851,0.00005664276,0.00002818275,0.00001066538,0.000002072335,0.00002237125,0.0001119845,0.9700763,0.0001920755,0.02920389,0.00008753408],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8461391,0.001078717,0.1521908,0.0001008053,0.00003444187,0.0001674816,0.00002096922,0.0000226959,0.0002449856],"genre_scores_gemma":[0.9731088,0.0001821962,0.02418692,0.0002228193,0.00002854439,0.00002136812,0.0000825352,0.00001581239,0.00215095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1280039,"threshold_uncertainty_score":0.4140313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006323480870093317,"score_gpt":0.3155764067520572,"score_spread":0.3092529258819638,"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."}}