{"id":"W3002762132","doi":"10.1103/physreva.102.043510","title":"Efficient molecule discrimination in electron microscopy through an optimized orbital angular momentum sorter","year":2020,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Horizon 2020; Horizon 2020 Framework Programme; European Commission","keywords":"Angular momentum; Electron; Observable; Benchmark (surveying); Electron microscope; Physics; Molecule; Phase space; Quantum state; Azimuthal quantum number; Quantum; Optics; Total angular momentum quantum number; Quantum mechanics; Atomic physics; Computer science; Angular momentum coupling","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005918612,0.0001678548,0.0003650566,0.0001877935,0.0003059991,0.0005950581,0.000751552,0.0005701312,0.001184717],"category_scores_gemma":[0.0006271421,0.0001402436,0.0002205016,0.000282449,0.0006951838,0.0007554063,0.0005656651,0.0005603291,0.0001789223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005115708,"about_ca_system_score_gemma":0.0006000024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005407778,"about_ca_topic_score_gemma":0.0007620651,"domain_scores_codex":[0.9998842,0.00003088364,0.000005611417,0.0000171684,0.00004492138,0.00001714248],"domain_scores_gemma":[0.9997994,0.0001005177,0.00002514244,0.00004088602,0.00001961386,0.00001434601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006303116,0.000189179,0.001450814,0.0001818217,0.00004216084,0.0002255571,0.0001661805,0.4077649,0.1783032,0.3540518,0.001149554,0.05584454],"study_design_scores_gemma":[0.0000646152,0.00007499041,0.0001761948,0.000006411587,0.000008976541,0.00005242398,0.00002296336,0.9235492,0.04005273,0.03387753,0.002093702,0.00002020252],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2229245,0.0001411539,0.7714764,0.0003721205,0.00004862773,0.00006716024,0.00008575364,0.0008801974,0.004004107],"genre_scores_gemma":[0.6783991,0.0001232629,0.3193577,0.0001144467,0.000008011026,0.00007953132,0.0000453103,0.00005302557,0.001819657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001184717,"threshold_uncertainty_score":0.003963232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01114689013781257,"score_gpt":0.3987874921325636,"score_spread":0.3876406019947511,"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."}}