{"id":"W4410467629","doi":"10.32388/tl92a7","title":"Review of: \"CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets\"","year":2025,"lang":"en","type":"peer-review","venue":"","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cryo-electron microscopy; High resolution; Computer science; Resolution (logic); Artificial intelligence; Geography; Biology; Biophysics; Remote sensing","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":[0.0001774197,0.0004955709,0.0009420514,0.00007351807,0.00008715389,0.000006895263,0.0004652714,0.0004319489,0.0001634572],"category_scores_gemma":[0.0001169886,0.0003943024,0.0002287228,0.0002173479,0.0001654671,0.000004646052,0.0003982281,0.0003848462,7.958342e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001007497,"about_ca_system_score_gemma":0.0003673559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009472727,"about_ca_topic_score_gemma":0.0009355705,"domain_scores_codex":[0.9977432,0.00009288485,0.0008117394,0.000751764,0.0002508351,0.0003495962],"domain_scores_gemma":[0.9973946,0.00001481778,0.000858904,0.001011138,0.0006478613,0.00007270646],"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":[0.0001339818,0.00004631279,0.00001130492,0.06155473,0.0001847286,0.000001330363,0.000007210928,0.000009685434,0.5397413,0.000142225,0.3883592,0.009808064],"study_design_scores_gemma":[0.0001637887,0.0002202618,0.00001329385,0.02315213,0.0001737564,0.00001243681,0.000002975679,0.000006286695,0.5804184,0.0001279466,0.3953815,0.0003272407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.009746105,0.8080136,0.1589573,0.002492078,0.0004106116,0.01088355,0.008426611,0.0001652652,0.0009049755],"genre_scores_gemma":[0.02390653,0.6591813,0.1702124,0.01001438,0.0006346387,0.001257186,0.06167477,0.0003045296,0.07281426],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1488322,"threshold_uncertainty_score":0.9998509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006171984106300518,"score_gpt":0.3079688805096912,"score_spread":0.3017968964033907,"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."}}