{"id":"W4417076392","doi":"10.1002/advs.202518706","title":"Biomechanics‐Driven 3D Architecture Inference from Histology Using CellSqueeze3D","year":2025,"lang":"en","type":"article","venue":"Advanced Science","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; Centre Scientifique et Technique du Bâtiment; Shanghai Jiao Tong University; Science and Technology Commission of Shanghai Municipality","keywords":"Inference; Pattern recognition (psychology); Classifier (UML); Random forest; Digital pathology; Particle swarm optimization; Support vector machine; Perspective (graphical)","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":[0.0001192359,0.0001285199,0.0001165944,0.0001416651,0.0002492816,0.00003301814,0.0004511358,0.0000841276,0.00003950354],"category_scores_gemma":[0.0002255531,0.0001274341,0.00005180354,0.0004196792,0.0001850605,0.00001247553,0.0002789478,0.0001289386,0.000009422626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005893987,"about_ca_system_score_gemma":0.0002801038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007309428,"about_ca_topic_score_gemma":0.0000664397,"domain_scores_codex":[0.9988718,0.00002983972,0.0001588438,0.0005341529,0.0001209824,0.0002843571],"domain_scores_gemma":[0.9992495,0.00002691276,0.00007280962,0.0004585294,0.0001226775,0.00006954036],"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":[0.00002181491,0.00002235253,0.00002373448,0.000002469804,0.000007303207,0.000001541314,0.00002054203,0.001153331,0.9883833,0.001012861,0.00002891294,0.009321863],"study_design_scores_gemma":[0.0002142162,0.00007938912,0.00003625787,0.00002355536,0.00001155441,0.000004619446,0.00003868357,0.01699566,0.9308119,0.002855932,0.04876387,0.0001643872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5692953,0.0004265012,0.4273753,0.0001226111,0.0009705744,0.0001208085,0.00002226611,0.00001800094,0.001648668],"genre_scores_gemma":[0.9745805,0.00007081634,0.0243769,0.0004159021,0.00004875348,0.000008064259,0.00002802074,0.000007934751,0.0004631339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4052852,"threshold_uncertainty_score":0.5196613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008589034521907544,"score_gpt":0.2950469737937686,"score_spread":0.286457939271861,"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."}}