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Record W2809819661 · doi:10.1093/jmicro/dfx089

Calculation, consequences and measurement of the point spread function for low-loss inelastic scattering

2017· article· en· W2809819661 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMicroscopy · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPoint spread functionOpticsInelastic scatteringElectron energy loss spectroscopyScatteringEnergy (signal processing)PhysicsComputational physicsDelocalized electronPoint (geometry)Contrast transfer functionMaterials scienceAtomic physicsSpherical aberrationMathematicsQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

We have previously derived an analytical formula for the point spread function (PSF) that describes the delocalization of low-loss inelastic scattering. Here, we modify the formula to take account variation of scattered-electron phase. The exponentially attenuated Lorentzian form is retained but its halfwidth at half maximum is chosen to provide better agreement with measurements of the median delocalization distance. For low energy losses, the 1/r2 tails of the PSF extend beyond the region of energy deposition, allowing a small-diameter electron probe to provide energy-loss data from relatively undamaged regions of a beam-sensitive specimen. Alternatively, a core-loss or elastic image can be recorded with less damage by sparse scanning, as in scanned moiré imaging. A procedure is proposed for directly measuring the PSF, using a TEM with aberration-corrected lenses and an energy-filtered imaging system.

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 categoriesnone
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 score0.214
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.312
Teacher spread0.298 · 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