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Record W3002762132 · doi:10.1103/physreva.102.043510

Efficient molecule discrimination in electron microscopy through an optimized orbital angular momentum sorter

2020· article· en· W3002762132 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysical review. A/Physical review, A · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of Ottawa
FundersHorizon 2020Horizon 2020 Framework ProgrammeEuropean Commission
KeywordsAngular momentumElectronObservableBenchmark (surveying)Electron microscopePhysicsMoleculePhase spaceQuantum stateAzimuthal quantum numberQuantumOpticsTotal angular momentum quantum numberQuantum mechanicsAtomic physicsComputer scienceAngular momentum coupling

Abstract

fetched live from OpenAlex

We reformulate the single-molecule analysis in an electron microscope in terms of a quantum-state discrimination problem, and discuss its implementation through electron-beam shaping. Our approach relies on the use of new electron-optical elements to efficiently extract the ``which-molecule'' information from the state of each electron. The optimal observables are formally derived, and subsequently implemented by suitably designed phase elements in a generalized orbital angular momentum sorter. As a representative example, we simulate the discrimination between model proteins and benchmark the performance of the sorter against that of the best known real-space approach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.399
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it